Facial Emotion Detection¶


Executive Summary¶


  • Project Aim: We developed a neural network model capable of identifying human emotions from facial expressions captured in images.
  • Data Utilization: Our model training utilized a dataset comprised of grayscale images, each labeled with one of four distinct emotions: happy, neutral, sad, or surprise.
  • Model Exploration: We engaged in extensive testing of various neural network architectures, including both custom-designed Convolutional Neural Networks (CNNs) and established transfer learning frameworks such as VGG16, ResNet50V2, and EfficientNetV2B0.
  • Performance Metrics: The models were rigorously evaluated on multiple metrics including accuracy, precision, recall, and F1-score, aided by detailed confusion matrices for in-depth analysis.
  • Best Performer: The standout model was a complex CNN featuring five convolutional blocks, which yielded the highest accuracy and performance metrics in tests.
  • Ethical Considerations: For future deployments of the emotion detection model, we raise concerns about ethical considerations, focusing on ensuring privacy and fairness in real-world applications.
  • Real-World Application: This model holds potential for significant impact in various sectors by enhancing user interface experiences and providing support tools for mental health professionals.
  • Future Work: We propose further refinement of the model and the integration of additional safeguards to address ethical concerns more robustly.


Context¶


Deep learning has been increasingly applied to tasks involving less structured data types like images, texts, audio, and video in recent years. These endeavors often aim to achieve human-like proficiency in processing such data, leveraging our innate ability to intelligently interact with complex, unstructured information. Within the realm of AI, a field known as Artificial Emotional Intelligence, or Emotion AI, focuses on creating technologies that can understand human emotions by analyzing body language, facial expressions, and voice tones, and respond to them effectively.

Recognizing facial expressions plays a vital role in human-computer interaction. Research indicates that facial expressions and other visual signals account for about 55% of how we convey emotions. Thus, developing a model capable of accurately recognizing facial emotions is a significant stride toward equipping machines with AI that exhibits emotionally intelligent behavior. Systems that can automatically recognize facial expressions have a broad range of potential applications, from understanding human behavior to diagnosing psychological conditions, and improving the interaction quality of virtual assistants in customer service settings.


Objective¶


The goal of this project is to use Deep Learning and Artificial Intelligence techniques to create a computer vision model that can accurately detect facial emotions. The model should be able to perform multi-class classification on images of facial expressions, to classify the expressions according to the associated emotion.


Key Questions¶


Throughout the project, we will be answering the following questions:

  • How accurately can the deep learning model identify and classify different facial emotions (happy, sad, surprise, neutral) from images?
  • How well does the model generalize to new, unseen images? Can it maintain high accuracy across the test, train, and validation datasets?
  • How does the different model architectures compare in terms of accuracy to classify the different emotions?
  • What are the potential applications of the developed model, and what implications might its deployment have in some industry fields?

Problem Formulation¶


We are tasked with leveraging Deep Learning techniques to develop a computer vision model capable of accurately detecting and classifying facial emotions. The model needs to distinguish between four specific emotions (happy, sad, surprise, neutral) based on images of facial expressions. This task involves multi-class classification, requiring the model to predict the correct category of emotion for each image it processes.


About the dataset¶


The data set consists of 3 folders, i.e., 'test', 'train', and 'validation'. Each of these folders has four subfolders:

‘happy’: Images of people who have happy facial expressions.
‘sad’: Images of people with sad or upset facial expressions.
‘surprise’: Images of people who have shocked or surprised facial expressions.
‘neutral’: Images of people showing no prominent emotion in their facial expression at all.

Importing the Libraries¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import zipfile
import random
from PIL import Image
from typing import List
from datetime import datetime

# For Data Visualization
import seaborn as sns

# For Model Building
import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential, Model  # Sequential API for sequential model
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input  # Importing different layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, LeakyReLU, ReLU
from tensorflow.keras import backend
from tensorflow.keras.optimizers import Adam, RMSprop  # Optimizers for optimizing the model
from tensorflow.keras.callbacks import EarlyStopping  # Regularization method to prevent the overfitting
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.preprocessing.image import load_img, ImageDataGenerator
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg16
from keras.applications import VGG16
from keras.applications.resnet_v2 import preprocess_input as preprocess_input_resnetv2
from keras.applications import ResNet50V2
from keras.applications.efficientnet_v2 import preprocess_input as preprocess_input_efficientnetv2
from keras.applications import EfficientNetV2B0
2024-04-11 00:04:06.669124: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-11 00:04:07.217980: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Let us load and unzip the data¶

Note:

  • You must download the dataset from the link provided on Olympus and upload the same on your Google drive before executing the code in the next cell.
  • In case of any error, please make sure that the path of the file is correct as the path may be different for you.
In [2]:
# Storing the path of the data file from the Google drive
path = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/Facial_emotion_images.zip"

# The data is provided as a zip file so we need to extract the files from the zip file
with zipfile.ZipFile(path, "r") as zip_ref:
    zip_ref.extractall()

Preparing the Data¶

The dataset has three folders, i.e., 'train', 'validation' and 'test'. Each of these folders has four sub-folders, namely 'happy', 'neutral', 'sad', and 'surprise'.

We will have the train, validation and test path stored in a variable named 'SUBDIRS', and a base directory 'DATADIR'.

The names of the sub-folders, which will be the classes for our classification task will be stored in an array called 'CATEGORIES'.

In [3]:
DATADIR = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/Facial_emotion_images"  # Base directory
SUBDIRS = ["train", "validation", "test"]  # Subdirectories
CATEGORIES = ["happy", "neutral", "sad", "surprise"]  # Emotion categories

We are going to check the size of one image, and then check if all the other images have the same size. In case, they are different, we'll resize the ones that are different.

In [4]:
def get_first_image_size(directory, sub_dirs, categories):
    """
    Returns the size of the first image found in the specified directories.

    Parameters:
    - directory (str): The base directory of the dataset.
    - sub_dirs (list of str): Subdirectories to search through (e.g., ['train', 'validation', 'test']).
    - categories (list of str): Categories (e.g., ['happy', 'neutral', 'sad', 'surprise']).

    Returns:
    - tuple: Size of the first image found (width, height).
    """
    for sub_dir in sub_dirs:
        for category in categories:
            path = os.path.join(directory, sub_dir, category)
            for img_name in os.listdir(path):
                img_path = os.path.join(path, img_name)
                with Image.open(img_path) as img:
                    return img.size  # Return the size of the first image found
In [5]:
# Get the size of the first image
expected_size = get_first_image_size(DATADIR, SUBDIRS, CATEGORIES)

print(f"Expected size of the first image: {expected_size}")
Expected size of the first image: (48, 48)
In [6]:
def check_image_sizes(directory, sub_dirs, categories, target_size):
    """
    Checks if all images in the specified directories match the target size.

    Parameters:
    - directory (str): The base directory of the dataset.
    - sub_dirs (list of str): Subdirectories to search through.
    - categories (list of str): Categories to search through.
    - target_size (tuple): The expected size of the images (width, height).

    Returns:
    - bool: True if all images match the target size, False otherwise.
    """
    all_match = True  # Flag to keep track of size match

    for sub_dir in sub_dirs:
        for category in categories:
            path = os.path.join(directory, sub_dir, category)
            for img_name in os.listdir(path):
                img_path = os.path.join(path, img_name)
                with Image.open(img_path) as img:
                    if img.size != target_size:
                        print(f"Image {img_path} has a different size: {img.size}, expected: {target_size}")
                        all_match = False
                        return all_match  # Return early upon first mismatch
    return all_match
In [7]:
# Check if all images match the expected size
all_match = check_image_sizes(DATADIR, SUBDIRS, CATEGORIES, expected_size)
if all_match:
    print("All images match the expected size.")
else:
    print("Not all images match the expected size.")
All images match the expected size.

Visualizing our Classes¶

Let's look at our classes.

Write down your observation for each class. What do you think can be a unique feature of each emotion, that separates it from the remaining classes?

In [8]:
def visualize_emotion_images(directory: str, sub_dirs: List[str], emotion: str, image_count: int = 9) -> None:
    """
    Visualizes a specified number of images from a given emotion class directory across specified subdirectories.

    Parameters:
    - directory (str): The base directory where emotion class folders are located across subdirectories.
    - sub_dirs (List[str]): List of subdirectories ('train', 'validation', 'test') to search through.
    - emotion (str): The specific emotion class to visualize images from.
    - image_count (int): The number of images to display. Defaults to 9.

    Returns:
    - None: This function does not return any value but displays images inline.
    """
    image_paths: List[str] = []  # To store paths of images to be displayed

    # Iterate through the specified subdirectories to collect image paths
    for sub_dir in sub_dirs:
        emotion_dir: str = os.path.join(directory, sub_dir, emotion)
        if os.path.isdir(emotion_dir):
            for img_name in os.listdir(emotion_dir):
                img_path = os.path.join(emotion_dir, img_name)
                image_paths.append(img_path)

    # If there are no images found for the emotion, print a message and return
    if not image_paths:
        print(f"No images found for the specified emotion: {emotion}")
        return

    # Select a random subset of image paths
    selected_image_paths: np.ndarray = np.random.choice(image_paths, min(image_count, len(image_paths)), replace=False)

    # Setup for plotting
    fig = plt.figure(figsize=(4, 4))
    columns: int = 3
    rows: int = image_count // columns + (1 if image_count % columns else 0)

    # Iterate over the selected images and display them
    for i, image_path in enumerate(selected_image_paths, start=1):
        ax = fig.add_subplot(rows, columns, i)
        image = load_img(image_path, target_size=(48, 48))  # Ensure the image is resized to 48x48
        plt.imshow(image)
        plt.axis("off")

    plt.tight_layout()
    plt.show()

Happy¶

In [9]:
visualize_emotion_images(DATADIR, SUBDIRS, "happy", 9)
No description has been provided for this image

Observations and Insights:

  • The images appear to be in grayscale and vary in terms of lighting, contrast, and clarity.

  • The images display a range of happy expressions, from broad smiles showing teeth to subtle smiles without teeth. Also a diversity of subjects in terms of age, gender and also ethnicity.

Sad¶

In [10]:
visualize_emotion_images(DATADIR, SUBDIRS, "sad", 9)
No description has been provided for this image

Observations and Insights:

  • The images capture a wide spectrum of sadness, from subtle, somber expressions to more overt manifestations like crying.
  • The dataset includes faces with different orientations and features. Some faces are directly looking at the camera, while others are tilted or partially turned away.

Neutral¶

In [11]:
visualize_emotion_images(DATADIR, SUBDIRS, "neutral", 9)
No description has been provided for this image

Observations and Insights:

  • The defining characteristic of these images is the absence of clear, expressive features that denote a specific emotion.
  • Some faces may have subtle features that could be misconstrued as expressing a mild emotion.

Surprised¶

In [12]:
visualize_emotion_images(DATADIR, SUBDIRS, "surprise", 9)
No description has been provided for this image

Observations and Insights:

  • The images showcase a range of intensities of surprise, from wide-eyed and open-mouthed expressions to more subdued, raised-eyebrow looks.
  • The subjects vary in age, including both infants and adults.

Checking Distribution of Classes¶

In [13]:
# Function to count images in each category
def count_images(data_dir, categories):
    counts = []
    for category in categories:
        path = os.path.join(data_dir, category)
        count = len([name for name in os.listdir(path) if os.path.isfile(os.path.join(path, name))])
        counts.append(count)
    return counts


SUBDIRS_DICT = {"train": "train", "validation": "validation", "test": "test"}

# Counting images in each dataset
train_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["train"]), CATEGORIES)
validation_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["validation"]), CATEGORIES)
test_counts = count_images(os.path.join(DATADIR, SUBDIRS_DICT["test"]), CATEGORIES)


# Create DataFrames and format for easier reading
def create_df(counts, categories, dataset_name):
    df = pd.DataFrame({"Class": categories, "Count": counts})
    df["Percentage"] = (df["Count"] / df["Count"].sum()) * 100
    df.set_index("Class", inplace=True)

    # Formatting for easier reading
    df["Count"] = df["Count"].apply(lambda x: f"{x:,}")  # Adds commas to thousands
    df["Percentage"] = df["Percentage"].apply(lambda x: f"{x:.2f}")  # Rounds to two decimals

    print(f"{dataset_name} Data Distribution:")
    print(df)
    total_images = df["Count"].str.replace(",", "").astype(int).sum()
    print(f"Total images in {dataset_name}: {total_images:,}\n")  # Formats total count with commas


create_df(train_counts, CATEGORIES, "Training")
create_df(validation_counts, CATEGORIES, "Validation")
create_df(test_counts, CATEGORIES, "Testing")
Training Data Distribution:
          Count Percentage
Class                     
happy     3,976      26.32
neutral   3,978      26.33
sad       3,982      26.36
surprise  3,173      21.00
Total images in Training: 15,109

Validation Data Distribution:
          Count Percentage
Class                     
happy     1,825      36.67
neutral   1,216      24.43
sad       1,139      22.89
surprise    797      16.01
Total images in Validation: 4,977

Testing Data Distribution:
         Count Percentage
Class                    
happy       32      25.00
neutral     32      25.00
sad         32      25.00
surprise    32      25.00
Total images in Testing: 128

Think About It:

  • Are the classes equally distributed? If not, do you think the imbalance is too high? Will it be a problem as we progress?
  • Are there any Exploratory Data Analysis tasks that we can do here? Would they provide any meaningful insights?

Observations and Insights:

  • Training Data: The training dataset shows a relatively balanced distribution among the classes of 'happy', 'neutral', and 'sad', each comprising approximately 26% of the dataset. However, 'surprise' is slightly underrepresented, making up 21% of the data. We'll see on the results if this is noticeable.
  • Validation Data: In the validation dataset, there's a more pronounced imbalance. 'Happy' expressions dominate at 36.67%, followed by 'neutral' at 24.43%, 'sad' at 22.89%, and 'surprise' at 16.01%. This distribution deviates more significantly from an even split, indicating a potential bias towards 'happy' expressions.
  • Testing Data: The testing dataset is perfectly balanced, with each class representing 25% of the data. This uniform distribution is ideal for evaluating the model's performance across all classes evenly.

Creating our Data Loaders¶

In this section, we are creating data loaders that we will use as inputs to our Neural Network.

You have two options for the color_mode. You can set it to color_mode = 'rgb' or color_mode = 'grayscale'. You will need to try out both and see for yourself which one gives better performance.

Tested data: We have tested both 'grayscale' and 'rgb', and we got better results with 'grayscale', which makes sense as the images are in grayscale.

In [14]:
# Set this to 'grayscale' as the images are in grayscale
color_mode = "grayscale"

# As we have checked, all images are 48x48, we will set the img_width and img_height to 48
img_width, img_height = 48, 48
color_layers = 1
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32

# Training Data Augmentation
train_datagen = ImageDataGenerator(
    rescale=1.0 / 255,  # Normalize pixel values to [0,1]
    horizontal_flip=True,  # Faces are symmetric; flipping can simulate looking from another direction
    brightness_range=(0.5, 1.5),  # Randomly adjust brightness to simulate different lighting conditions
    shear_range=0.3,  # Shear transformations for perspective changes
    rotation_range=20,  # Slight rotation to introduce variability without distorting emotion features
    width_shift_range=0.1,  # Slight horizontal shifts to simulate off-center faces
    height_shift_range=0.1,  # Slight vertical shifts to account for different heights/angles
    zoom_range=0.1,  # Small zoom in/out to simulate closer or further away faces
)

# Validation and Testing Data should not be augmented!
validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_datagen = ImageDataGenerator(rescale=1.0 / 255)

# Creating train_dir, validation_dir, and test_dir directories using the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])

# Train Generator
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
)

# Validation Generator
validation_generator = validation_datagen.flow_from_directory(
    validation_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for evaluation
)

# Testing Generator
test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.

Let's look at some examples of a batch of augmented training data.

In [15]:
# Fetch a batch of images and labels
images, labels = next(train_generator)

# Assuming the labels are one-hot encoded, we need to convert them back to class indices
labels_indices = labels.argmax(axis=1)

# Mapping of indices to class names, based on the 'class_indices' attribute of the generator
index_to_class = {v: k for k, v in train_generator.class_indices.items()}

fig, axes = plt.subplots(4, 4, figsize=(8, 8))
for image, label_index, ax in zip(images, labels_indices, axes.flatten()):
    ax.imshow(image.squeeze(), cmap="gray")  # Squeeze and cmap for grayscale
    class_name = index_to_class[label_index]
    ax.set_title(class_name)
    ax.axis("off")

plt.tight_layout()
plt.show()
No description has been provided for this image

Model Building¶

Think About It:

  • Are Convolutional Neural Networks the right approach? Should we have gone with Artificial Neural Networks instead?

Answer: Convolutional Neural Networks (CNNs) are the right approach for facial emotion classification on images, as they excel at capturing spatial hierarchies and patterns in visual data, which is critical for this type of task, unlike traditional Artificial Neural Networks (ANNs) which do not inherently process spatial information.

  • What are the advantages of CNNs over ANNs and are they applicable here?

    Answer: CNNs have the advantage of being able to automatically and efficiently learn spatial hierarchies of features from images (thanks to their convolutional layers and shared weights), making them particularly suitable for image-based tasks like facial emotion classification, where recognizing spatial relationships and patterns within the images is key to accurate classification.

Creating the Base Neural Network¶

Model 1 Architecture:¶

  • This is the first CNN Model, designed with a sequential architecture comprising three convolutional blocks, each followed by max-pooling and dropout layers for feature extraction and regularization.
  • The first convolutional block starts with a Conv2D layer having 64 filters and a 3x3 kernel size, utilizing 'relu' activation and 'same' padding to maintain the input size, paired with a MaxPooling2D layer with a 2x2 pool size and 'same' padding, and a Dropout layer with a rate of 0.2 to prevent overfitting.
  • The second convolutional block includes a Conv2D layer with 32 filters, a 3x3 kernel size, 'relu' activation, and 'same' padding, followed by a MaxPooling2D layer with a 2x2 pool size, 'same' padding, and another Dropout layer with a rate of 0.2.
  • Similarly, the third convolutional block mirrors the second, with a Conv2D layer also having 32 filters, a 3x3 kernel size, 'relu' activation, and 'same' padding, a subsequent MaxPooling2D layer with a 2x2 pool size, 'same' padding, and a Dropout layer with a rate of 0.2.
  • After extracting features through convolutional blocks, the model flattens the output to feed into fully connected layers for classification.
  • The dense layers include a first Dense layer with 512 neurons and 'relu' activation, followed by a second Dense layer with 64 neurons and 'relu' activation, culminating in a final Dense layer with 4 neurons corresponding to the number of classes, using 'softmax' activation for multi-class classification.
  • The model employs the Adam optimizer with a learning rate of 0.001 to adjust weights and minimize the loss function during training.
In [16]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [17]:
# Intializing a sequential model
model_1 = Sequential()

model_1.add(Input(shape=(img_width, img_height, color_layers)))

model_1.add(Conv2D(64, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))

# Adding second conv layer with 32 filters
model_1.add(Conv2D(32, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))

# Add third conv layer with 32 filters and kernel size 3x3, padding 'same' followed by a Maxpooling2D layer
model_1.add(Conv2D(32, (3, 3), activation="relu", padding="same"))
model_1.add(MaxPooling2D((2, 2), padding="same"))
model_1.add(Dropout(0.2))

# Flattening the output of the conv layer after max pooling to make it ready for creating dense connections
model_1.add(Flatten())

# Adding fully connected dense layers
model_1.add(Dense(512, activation="relu"))
model_1.add(Dense(64, activation="relu"))

# Adding output layer
model_1.add(Dense(4, activation="softmax"))

# Using Adam Optimizer
opt = Adam(learning_rate=0.001)
2024-04-11 00:04:11.726218: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.747212: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.747375: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.747960: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.748070: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.748171: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.802454: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.802581: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.802685: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-04-11 00:04:11.802760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6272 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:06:00.0, compute capability: 8.6

Compiling and Training the Model¶

In [18]:
# Compiling the model
model_1.compile(optimizer=opt, loss="categorical_crossentropy", metrics=["accuracy"])

# Generating the summary of the model
model_1.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 48, 48, 64)     │           640 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 24, 24, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 24, 24, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │        18,464 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 32)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 12, 12, 32)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 12, 12, 32)     │         9,248 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_2 (MaxPooling2D)  │ (None, 6, 6, 32)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 6, 32)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 1152)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 512)            │       590,336 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 64)             │        32,832 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 651,780 (2.49 MB)
 Trainable params: 651,780 (2.49 MB)
 Non-trainable params: 0 (0.00 B)
In [19]:
results_path = "/home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results"
In [20]:
class DelayedEarlyStopping(EarlyStopping):
    """Stop training when a monitored metric has stopped improving after a certain number of epochs.

    Arguments:
        monitor: Quantity to be monitored.
        min_delta: Minimum change in the monitored quantity to qualify as an improvement,
                   i.e., an absolute change of less than min_delta will count as no improvement.
        patience: Number of epochs with no improvement after which training will be stopped.
        verbose: Verbosity mode.
        mode: One of `{'auto', 'min', 'max'}`. In `min` mode, training will stop when the
              quantity monitored has stopped decreasing; in `max` mode it will stop when the
              quantity monitored has stopped increasing; in `auto` mode, the direction is
              automatically inferred from the name of the monitored quantity.
        baseline: Baseline value for the monitored quantity. Training will stop if the model
                  doesn't show improvement over the baseline.
        restore_best_weights: Whether to restore model weights from the epoch with the best value
                              of the monitored quantity.
        start_epoch: The epoch on which to start considering early stopping. Before this epoch,
                     early stopping will not be considered. This ensures that early stopping
                     checks only after a certain number of epochs.
    """

    def __init__(
        self,
        monitor="val_loss",
        min_delta=0,
        patience=0,
        verbose=0,
        mode="auto",
        baseline=None,
        restore_best_weights=False,
        start_epoch=30,
    ):
        super().__init__(
            monitor=monitor,
            min_delta=min_delta,
            patience=patience,
            verbose=verbose,
            mode=mode,
            baseline=baseline,
            restore_best_weights=restore_best_weights,
        )
        self.start_epoch = start_epoch

    def on_epoch_end(self, epoch, logs=None):
        # Override the original `on_epoch_end` method to include `start_epoch` logic.

        # If the current epoch is less than the start epoch, skip the early stopping check
        if epoch < self.start_epoch:
            return

        # Call the parent class method to perform the regular early stopping checks after the start epoch
        super().on_epoch_end(epoch, logs)
In [21]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

# Define the saving the best model callback
mc = ModelCheckpoint(
    f"{results_path}/best_model_1_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 45 epochs and using validation set
history_1 = model_1.fit(
    train_generator,
    epochs=45,
    validation_data=validation_generator,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/45
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1712793853.651159 1474977 service.cc:145] XLA service 0x7f226c00e760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1712793853.651180 1474977 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3070, Compute Capability 8.6
2024-04-11 00:04:13.681751: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-04-11 00:04:13.811989: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1712793855.129443 1475073 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1953', 4 bytes spill stores, 4 bytes spill loads

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I0000 00:00:1712793857.604780 1474977 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 1: val_accuracy improved from -inf to 0.37472, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 16s 24ms/step - accuracy: 0.2724 - loss: 1.3787 - val_accuracy: 0.3747 - val_loss: 1.2493 - learning_rate: 0.0010
Epoch 2/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.5000 - loss: 1.1636

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Epoch 2: val_accuracy improved from 0.37472 to 0.47720, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.3772 - loss: 1.2763 - val_accuracy: 0.4772 - val_loss: 1.1544 - learning_rate: 0.0010
Epoch 3/45
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Epoch 3: val_accuracy improved from 0.47720 to 0.55053, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

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Epoch 4/45
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Epoch 4: val_accuracy improved from 0.55053 to 0.58549, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4784 - loss: 1.1409 - val_accuracy: 0.5855 - val_loss: 0.9747 - learning_rate: 0.0010
Epoch 5/45
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Epoch 5: val_accuracy improved from 0.58549 to 0.58891, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5028 - loss: 1.0931 - val_accuracy: 0.5889 - val_loss: 0.9510 - learning_rate: 0.0010
Epoch 6/45
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Epoch 6: val_accuracy improved from 0.58891 to 0.60137, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5267 - loss: 1.0534 - val_accuracy: 0.6014 - val_loss: 0.9226 - learning_rate: 0.0010
Epoch 7/45
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Epoch 7: val_accuracy improved from 0.60137 to 0.63392, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5414 - loss: 1.0388 - val_accuracy: 0.6339 - val_loss: 0.8682 - learning_rate: 0.0010
Epoch 8/45
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Epoch 8: val_accuracy improved from 0.63392 to 0.64657, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5506 - loss: 1.0149 - val_accuracy: 0.6466 - val_loss: 0.8343 - learning_rate: 0.0010
Epoch 9/45
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Epoch 9: val_accuracy improved from 0.64657 to 0.64838, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5641 - loss: 0.9965 - val_accuracy: 0.6484 - val_loss: 0.8419 - learning_rate: 0.0010
Epoch 10/45
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290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5858 - loss: 0.9718

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5857 - loss: 0.9720

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5856 - loss: 0.9721

305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5854 - loss: 0.9723

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5853 - loss: 0.9724

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5852 - loss: 0.9725

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5851 - loss: 0.9727

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5849 - loss: 0.9728

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5848 - loss: 0.9729

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5847 - loss: 0.9730

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5846 - loss: 0.9731

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5845 - loss: 0.9732

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5844 - loss: 0.9733

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5843 - loss: 0.9734

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5843 - loss: 0.9734

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5842 - loss: 0.9735

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5841 - loss: 0.9736

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5840 - loss: 0.9736

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5840 - loss: 0.9737

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5839 - loss: 0.9738

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5839 - loss: 0.9738

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5838 - loss: 0.9738

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5838 - loss: 0.9739

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5837 - loss: 0.9739

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5837 - loss: 0.9739

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5836 - loss: 0.9740

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5836 - loss: 0.9741

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5835 - loss: 0.9741

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5835 - loss: 0.9742

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5834 - loss: 0.9743

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5833 - loss: 0.9743

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5833 - loss: 0.9744

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5832 - loss: 0.9744

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5831 - loss: 0.9745

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5831 - loss: 0.9746

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5830 - loss: 0.9746
Epoch 10: val_accuracy did not improve from 0.64838

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5828 - loss: 0.9748 - val_accuracy: 0.6383 - val_loss: 0.8619 - learning_rate: 0.0010
Epoch 11/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5000 - loss: 1.0313

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5612 - loss: 1.0064  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5735 - loss: 0.9932

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5725 - loss: 0.9904

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5713 - loss: 0.9871

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5715 - loss: 0.9857

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5711 - loss: 0.9852

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5707 - loss: 0.9856

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5697 - loss: 0.9870

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5689 - loss: 0.9885

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5686 - loss: 0.9895

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5687 - loss: 0.9897

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5690 - loss: 0.9893

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5695 - loss: 0.9889

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5700 - loss: 0.9886

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5705 - loss: 0.9884

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5709 - loss: 0.9882

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5713 - loss: 0.9882

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5717 - loss: 0.9882

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5721 - loss: 0.9882

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5724 - loss: 0.9883

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5727 - loss: 0.9884

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5732 - loss: 0.9885

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5737 - loss: 0.9885

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5742 - loss: 0.9884

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5747 - loss: 0.9882

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5752 - loss: 0.9880

132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5757 - loss: 0.9878

137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5761 - loss: 0.9875

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5765 - loss: 0.9873

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5768 - loss: 0.9871

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5771 - loss: 0.9869

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5775 - loss: 0.9867

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5778 - loss: 0.9865

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5781 - loss: 0.9863

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5784 - loss: 0.9861

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5786 - loss: 0.9859

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5788 - loss: 0.9857

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5790 - loss: 0.9855

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5792 - loss: 0.9853

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5794 - loss: 0.9852

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5795 - loss: 0.9850

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5797 - loss: 0.9848

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5798 - loss: 0.9847

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5799 - loss: 0.9846

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9844

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9843

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5800 - loss: 0.9842

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9841

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9840

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9839

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5799 - loss: 0.9838

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9837

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9836

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9835

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9834

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9833

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9831

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9830

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9829

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9828

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5798 - loss: 0.9826

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9825

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9823

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5798 - loss: 0.9822

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9821

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9819

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9818

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5799 - loss: 0.9816

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9815

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9813

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9812

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9810

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5800 - loss: 0.9809

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9807

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9806

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5801 - loss: 0.9804

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5802 - loss: 0.9802

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5802 - loss: 0.9800

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5803 - loss: 0.9799

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9797

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9796

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5803 - loss: 0.9794

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9793

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9792

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5804 - loss: 0.9791

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Epoch 11: val_accuracy improved from 0.64838 to 0.65943, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5805 - loss: 0.9778 - val_accuracy: 0.6594 - val_loss: 0.8030 - learning_rate: 0.0010
Epoch 12/45
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Epoch 12: val_accuracy improved from 0.65943 to 0.66667, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5918 - loss: 0.9552 - val_accuracy: 0.6667 - val_loss: 0.7943 - learning_rate: 0.0010
Epoch 13/45
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Epoch 13: val_accuracy improved from 0.66667 to 0.67109, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5927 - loss: 0.9443 - val_accuracy: 0.6711 - val_loss: 0.8001 - learning_rate: 0.0010
Epoch 14/45
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205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5957 - loss: 0.9506

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5958 - loss: 0.9505

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5958 - loss: 0.9503

220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5959 - loss: 0.9500

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5960 - loss: 0.9499

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5961 - loss: 0.9497

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5963 - loss: 0.9495

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5964 - loss: 0.9493

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5965 - loss: 0.9492

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5966 - loss: 0.9490

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5967 - loss: 0.9489

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5968 - loss: 0.9487

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5969 - loss: 0.9486

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5970 - loss: 0.9485

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5970 - loss: 0.9484

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5971 - loss: 0.9483

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5972 - loss: 0.9482

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5972 - loss: 0.9481

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5973 - loss: 0.9480

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5973 - loss: 0.9479

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5974 - loss: 0.9479

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5974 - loss: 0.9478

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5974 - loss: 0.9478

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9477

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9477

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9476

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5975 - loss: 0.9475

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9474

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9474

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9473

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9473

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9472

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9472

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5976 - loss: 0.9471

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9470

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9470

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9469

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9468

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5977 - loss: 0.9468

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9467

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9467

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5977 - loss: 0.9466

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9465

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9465

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9464

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9463

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9462

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5978 - loss: 0.9461

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9461

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9460

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9459

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5979 - loss: 0.9458

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9457

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9457

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9456

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5980 - loss: 0.9455
Epoch 14: val_accuracy did not improve from 0.67109

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5980 - loss: 0.9454 - val_accuracy: 0.6649 - val_loss: 0.7973 - learning_rate: 0.0010
Epoch 15/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5625 - loss: 0.9892

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5883 - loss: 0.9528  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5941 - loss: 0.9413

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5969 - loss: 0.9385

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5983 - loss: 0.9376

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5988 - loss: 0.9390

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5991 - loss: 0.9393

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5992 - loss: 0.9389

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5994 - loss: 0.9377

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5992 - loss: 0.9363

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5992 - loss: 0.9356

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9352

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5990 - loss: 0.9346

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9343

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5989 - loss: 0.9340

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5987 - loss: 0.9338

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5985 - loss: 0.9340

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5984 - loss: 0.9343

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5982 - loss: 0.9346

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5981 - loss: 0.9348

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5978 - loss: 0.9351

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9351

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9350

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5977 - loss: 0.9350

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5976 - loss: 0.9350

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.5975 - loss: 0.9351

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5975 - loss: 0.9350

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5975 - loss: 0.9349

135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5977 - loss: 0.9346

140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5978 - loss: 0.9343

144/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5979 - loss: 0.9340

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9337

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5980 - loss: 0.9338

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5981 - loss: 0.9338

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5982 - loss: 0.9337

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5982 - loss: 0.9337

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9338

223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5983 - loss: 0.9337

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5983 - loss: 0.9337

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5984 - loss: 0.9337

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9336

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9336

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5985 - loss: 0.9337

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9337

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9336

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5986 - loss: 0.9336

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5987 - loss: 0.9336

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5987 - loss: 0.9335

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5988 - loss: 0.9335

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.5988 - loss: 0.9334

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5989 - loss: 0.9333

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Epoch 15: val_accuracy improved from 0.67109 to 0.68093, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5998 - loss: 0.9322 - val_accuracy: 0.6809 - val_loss: 0.7717 - learning_rate: 0.0010
Epoch 16/45
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Epoch 16: val_accuracy improved from 0.68093 to 0.68877, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6000 - loss: 0.9249 - val_accuracy: 0.6888 - val_loss: 0.7655 - learning_rate: 0.0010
Epoch 17/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.5000 - loss: 0.8790

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463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6165 - loss: 0.9077
Epoch 17: val_accuracy did not improve from 0.68877

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6163 - loss: 0.9077 - val_accuracy: 0.6711 - val_loss: 0.7998 - learning_rate: 0.0010
Epoch 18/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.7676

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Epoch 18: val_accuracy improved from 0.68877 to 0.69078, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6185 - loss: 0.9040 - val_accuracy: 0.6908 - val_loss: 0.7475 - learning_rate: 0.0010
Epoch 19/45
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209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8879

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6312 - loss: 0.8880

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6311 - loss: 0.8882

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6309 - loss: 0.8884

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6308 - loss: 0.8886

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6307 - loss: 0.8888

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6306 - loss: 0.8890

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6305 - loss: 0.8891

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6303 - loss: 0.8893

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6302 - loss: 0.8895

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6301 - loss: 0.8896

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6300 - loss: 0.8897

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6299 - loss: 0.8898

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6298 - loss: 0.8899

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6297 - loss: 0.8900

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6296 - loss: 0.8901

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6295 - loss: 0.8901

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6294 - loss: 0.8902

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6293 - loss: 0.8903

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6292 - loss: 0.8904

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6290 - loss: 0.8905

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6289 - loss: 0.8906

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6288 - loss: 0.8907

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6286 - loss: 0.8908

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6285 - loss: 0.8909

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6284 - loss: 0.8910

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6283 - loss: 0.8911

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6283 - loss: 0.8912

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6282 - loss: 0.8913

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6281 - loss: 0.8913

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6280 - loss: 0.8914

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6279 - loss: 0.8914

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6279 - loss: 0.8914

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6278 - loss: 0.8915

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6277 - loss: 0.8915

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6277 - loss: 0.8915

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6276 - loss: 0.8915

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6276 - loss: 0.8915

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6275 - loss: 0.8915

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6275 - loss: 0.8915

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6274 - loss: 0.8915

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6274 - loss: 0.8915

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6273 - loss: 0.8915

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6272 - loss: 0.8915

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6272 - loss: 0.8915

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6271 - loss: 0.8916

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6270 - loss: 0.8916

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6270 - loss: 0.8916
Epoch 19: val_accuracy did not improve from 0.69078

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6269 - loss: 0.8916 - val_accuracy: 0.6856 - val_loss: 0.7629 - learning_rate: 0.0010
Epoch 20/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 98ms/step - accuracy: 0.5625 - loss: 0.9138

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5675 - loss: 0.9293 

  8/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.5744 - loss: 0.9145

 11/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5805 - loss: 0.9089

 16/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.5872 - loss: 0.9070

 21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.5899 - loss: 0.9064

 26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.5909 - loss: 0.9059

 31/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.5938 - loss: 0.9028

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5964 - loss: 0.8991

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5990 - loss: 0.8958

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6010 - loss: 0.8936

 51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6024 - loss: 0.8925

 56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6034 - loss: 0.8919

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6040 - loss: 0.8914

 65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6048 - loss: 0.8908

 70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6055 - loss: 0.8902

 75/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6061 - loss: 0.8900

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6064 - loss: 0.8901

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6066 - loss: 0.8906

 88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6068 - loss: 0.8907

 93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6072 - loss: 0.8908

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6075 - loss: 0.8910

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6078 - loss: 0.8911

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6080 - loss: 0.8914

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6081 - loss: 0.8916

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6083 - loss: 0.8917

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6085 - loss: 0.8919

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6087 - loss: 0.8921

133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6089 - loss: 0.8922

138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6092 - loss: 0.8924

143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6094 - loss: 0.8925

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6098 - loss: 0.8925

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6102 - loss: 0.8923

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6105 - loss: 0.8922

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6109 - loss: 0.8920

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6112 - loss: 0.8918

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6115 - loss: 0.8916

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6118 - loss: 0.8914

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6120 - loss: 0.8912

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6122 - loss: 0.8911

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6124 - loss: 0.8909

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6125 - loss: 0.8908

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6127 - loss: 0.8907

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6128 - loss: 0.8906

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6130 - loss: 0.8905

219/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6131 - loss: 0.8905

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6132 - loss: 0.8904

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6134 - loss: 0.8904

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6136 - loss: 0.8903

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6138 - loss: 0.8902

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6139 - loss: 0.8902

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6141 - loss: 0.8901

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6143 - loss: 0.8900

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6144 - loss: 0.8899

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6146 - loss: 0.8898

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6148 - loss: 0.8897

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6149 - loss: 0.8897

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6151 - loss: 0.8896

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6152 - loss: 0.8896

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6153 - loss: 0.8895

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6154 - loss: 0.8895

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6155 - loss: 0.8894

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6156 - loss: 0.8894

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6157 - loss: 0.8894

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6158 - loss: 0.8894

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6159 - loss: 0.8894

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6160 - loss: 0.8894

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6160 - loss: 0.8894

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6161 - loss: 0.8895

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6161 - loss: 0.8895

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6162 - loss: 0.8896

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6163 - loss: 0.8897

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6163 - loss: 0.8897

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6164 - loss: 0.8897

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6165 - loss: 0.8897

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6165 - loss: 0.8897

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6166 - loss: 0.8897

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6166 - loss: 0.8897

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6167 - loss: 0.8897

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6167 - loss: 0.8898

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.8898

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.8899

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6168 - loss: 0.8899

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8899

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8900

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8900

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8901

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6169 - loss: 0.8901

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8902

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8902

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8902

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8903

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6170 - loss: 0.8903
Epoch 20: val_accuracy did not improve from 0.69078

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6171 - loss: 0.8903 - val_accuracy: 0.6872 - val_loss: 0.7617 - learning_rate: 0.0010
Epoch 21/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.7500 - loss: 0.6629

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6591 - loss: 0.7993  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6492 - loss: 0.8362

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6465 - loss: 0.8480

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6440 - loss: 0.8541

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6448 - loss: 0.8542

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6440 - loss: 0.8550

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6426 - loss: 0.8566

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6418 - loss: 0.8568

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6412 - loss: 0.8567

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6406 - loss: 0.8570

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6397 - loss: 0.8578

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6391 - loss: 0.8582

 63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6385 - loss: 0.8585

 66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6380 - loss: 0.8588

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6371 - loss: 0.8597

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6364 - loss: 0.8604

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6359 - loss: 0.8612

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6354 - loss: 0.8623

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6349 - loss: 0.8633

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6345 - loss: 0.8642

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6341 - loss: 0.8650

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6337 - loss: 0.8657

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6333 - loss: 0.8662

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6329 - loss: 0.8665

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6326 - loss: 0.8670

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6322 - loss: 0.8674

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6318 - loss: 0.8680

135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6314 - loss: 0.8685

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6310 - loss: 0.8690

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6307 - loss: 0.8694

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6304 - loss: 0.8697

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6301 - loss: 0.8701

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6297 - loss: 0.8706

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6295 - loss: 0.8710

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6292 - loss: 0.8714

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6289 - loss: 0.8717

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6287 - loss: 0.8720

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6284 - loss: 0.8723

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6282 - loss: 0.8727

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6279 - loss: 0.8730

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6277 - loss: 0.8733

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6275 - loss: 0.8735

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6273 - loss: 0.8737

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6271 - loss: 0.8739

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6270 - loss: 0.8741

221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6268 - loss: 0.8743

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6267 - loss: 0.8746

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6265 - loss: 0.8748

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6264 - loss: 0.8749

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6263 - loss: 0.8751

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6262 - loss: 0.8753

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6261 - loss: 0.8755

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6260 - loss: 0.8756

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6259 - loss: 0.8758

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6259 - loss: 0.8759

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6258 - loss: 0.8760

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6258 - loss: 0.8761

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6257 - loss: 0.8763

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6256 - loss: 0.8764

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6256 - loss: 0.8765

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8767

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8768

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6255 - loss: 0.8769

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6254 - loss: 0.8770

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6254 - loss: 0.8771

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8772

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8773

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8774

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6253 - loss: 0.8775

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8775

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8776

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8776

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8777

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8777

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8778

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8779

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8779

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8780

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8780

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8781

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6252 - loss: 0.8781

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8782

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8782

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8783

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8783

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8783

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8784

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8784

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8784

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6251 - loss: 0.8785

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8785

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8786

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6250 - loss: 0.8786

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457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6249 - loss: 0.8787

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Epoch 21: val_accuracy improved from 0.69078 to 0.69982, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6249 - loss: 0.8789 - val_accuracy: 0.6998 - val_loss: 0.7321 - learning_rate: 0.0010
Epoch 22/45
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Epoch 22: val_accuracy improved from 0.69982 to 0.70404, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6323 - loss: 0.8813 - val_accuracy: 0.7040 - val_loss: 0.7202 - learning_rate: 0.0010
Epoch 23/45
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 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6405 - loss: 0.8447

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6403 - loss: 0.8451

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6401 - loss: 0.8457

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6399 - loss: 0.8461

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6397 - loss: 0.8466

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6395 - loss: 0.8471

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6393 - loss: 0.8479

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6391 - loss: 0.8486

133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6389 - loss: 0.8493

138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6387 - loss: 0.8499

143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6385 - loss: 0.8506

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6383 - loss: 0.8512

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6382 - loss: 0.8518

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6379 - loss: 0.8524

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6378 - loss: 0.8527

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6377 - loss: 0.8530

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6376 - loss: 0.8534

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6374 - loss: 0.8538

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6372 - loss: 0.8541

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6371 - loss: 0.8544

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6368 - loss: 0.8549

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6366 - loss: 0.8555

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6364 - loss: 0.8559

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6362 - loss: 0.8564

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6360 - loss: 0.8568

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6358 - loss: 0.8573

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6355 - loss: 0.8578

222/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6353 - loss: 0.8582

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6352 - loss: 0.8586

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6350 - loss: 0.8589

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6349 - loss: 0.8592

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6347 - loss: 0.8595

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6346 - loss: 0.8599

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6344 - loss: 0.8602

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6343 - loss: 0.8606

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6341 - loss: 0.8609

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6340 - loss: 0.8613

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6339 - loss: 0.8616

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6337 - loss: 0.8619

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6336 - loss: 0.8623

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6334 - loss: 0.8626

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6333 - loss: 0.8629

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6331 - loss: 0.8632

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6330 - loss: 0.8636

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6328 - loss: 0.8639

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6327 - loss: 0.8641

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6326 - loss: 0.8644

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6325 - loss: 0.8647

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6323 - loss: 0.8649

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6322 - loss: 0.8652

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6321 - loss: 0.8655

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6320 - loss: 0.8657

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8660

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8662

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6317 - loss: 0.8665

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6316 - loss: 0.8667

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6314 - loss: 0.8670

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6313 - loss: 0.8672

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6312 - loss: 0.8675

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6311 - loss: 0.8677

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6310 - loss: 0.8679

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6309 - loss: 0.8681

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6308 - loss: 0.8684

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6307 - loss: 0.8686

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6306 - loss: 0.8688

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6305 - loss: 0.8690

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6304 - loss: 0.8691

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6303 - loss: 0.8693

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6303 - loss: 0.8695

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6302 - loss: 0.8697

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6301 - loss: 0.8698

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6300 - loss: 0.8700

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6300 - loss: 0.8702

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6299 - loss: 0.8703

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6299 - loss: 0.8705

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6298 - loss: 0.8706

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6298 - loss: 0.8708
Epoch 23: val_accuracy did not improve from 0.70404

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6297 - loss: 0.8711 - val_accuracy: 0.6918 - val_loss: 0.7534 - learning_rate: 0.0010
Epoch 24/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6875 - loss: 0.8393

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6451 - loss: 0.8248  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6421 - loss: 0.8249

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6387 - loss: 0.8284

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6373 - loss: 0.8303

 24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6377 - loss: 0.8315

 28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6394 - loss: 0.8318

 33/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8339

 38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8370

 42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6402 - loss: 0.8394

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6399 - loss: 0.8423

 51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6399 - loss: 0.8452

 56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6400 - loss: 0.8470

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6403 - loss: 0.8478

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6408 - loss: 0.8481

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6415 - loss: 0.8480

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6418 - loss: 0.8482

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6420 - loss: 0.8487

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6420 - loss: 0.8490

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6421 - loss: 0.8491

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6421 - loss: 0.8490

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6422 - loss: 0.8488

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8486

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8486

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6424 - loss: 0.8485

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6425 - loss: 0.8485

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6425 - loss: 0.8486

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6424 - loss: 0.8488

132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6423 - loss: 0.8490

137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6422 - loss: 0.8492

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6422 - loss: 0.8494

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8496

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8499

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8501

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8504

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6421 - loss: 0.8506

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6420 - loss: 0.8509

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6420 - loss: 0.8512

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6419 - loss: 0.8514

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6419 - loss: 0.8516

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6418 - loss: 0.8518

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6418 - loss: 0.8520

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6417 - loss: 0.8522

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Epoch 24: val_accuracy improved from 0.70404 to 0.70605, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6372 - loss: 0.8614 - val_accuracy: 0.7060 - val_loss: 0.7095 - learning_rate: 0.0010
Epoch 25/45
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Epoch 25: val_accuracy improved from 0.70605 to 0.70645, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

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Epoch 26/45
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Epoch 26: val_accuracy did not improve from 0.70645

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6387 - loss: 0.8491 - val_accuracy: 0.7016 - val_loss: 0.7328 - learning_rate: 0.0010
Epoch 27/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 102ms/step - accuracy: 0.5938 - loss: 0.9629

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Epoch 27: val_accuracy improved from 0.70645 to 0.71087, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6416 - loss: 0.8574 - val_accuracy: 0.7109 - val_loss: 0.7201 - learning_rate: 0.0010
Epoch 28/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5000 - loss: 0.9681

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Epoch 28: val_accuracy improved from 0.71087 to 0.71167, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6445 - loss: 0.8524 - val_accuracy: 0.7117 - val_loss: 0.7050 - learning_rate: 0.0010
Epoch 29/45
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241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6469 - loss: 0.8339

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6469 - loss: 0.8340

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6468 - loss: 0.8342

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6467 - loss: 0.8344

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6466 - loss: 0.8345

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6465 - loss: 0.8347

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6464 - loss: 0.8349

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6463 - loss: 0.8350

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6463 - loss: 0.8351

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6462 - loss: 0.8353

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6462 - loss: 0.8354

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6461 - loss: 0.8356

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6461 - loss: 0.8357

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6460 - loss: 0.8359

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8360

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8361

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6459 - loss: 0.8363

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6458 - loss: 0.8364

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6457 - loss: 0.8366

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6457 - loss: 0.8367

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6456 - loss: 0.8369

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6456 - loss: 0.8370

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6455 - loss: 0.8372

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6455 - loss: 0.8373

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6454 - loss: 0.8375

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6453 - loss: 0.8376

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6453 - loss: 0.8378

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6452 - loss: 0.8379

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6452 - loss: 0.8381

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6451 - loss: 0.8382

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6451 - loss: 0.8384

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6450 - loss: 0.8385

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6449 - loss: 0.8387

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6449 - loss: 0.8389

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6448 - loss: 0.8390

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6447 - loss: 0.8392

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6447 - loss: 0.8393

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6446 - loss: 0.8394

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6446 - loss: 0.8396

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6445 - loss: 0.8397

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6445 - loss: 0.8398

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6444 - loss: 0.8399

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6444 - loss: 0.8400

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8402

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8402

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6443 - loss: 0.8403

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6442 - loss: 0.8404
Epoch 29: val_accuracy did not improve from 0.71167

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6442 - loss: 0.8406 - val_accuracy: 0.7099 - val_loss: 0.7116 - learning_rate: 0.0010
Epoch 30/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 45s 97ms/step - accuracy: 0.6250 - loss: 1.0577

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6103 - loss: 0.9443 

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6072 - loss: 0.9184

 13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6094 - loss: 0.9118

 16/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6121 - loss: 0.9075

 19/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6150 - loss: 0.9035

 24/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.6196 - loss: 0.8973

 30/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6232 - loss: 0.8878

 35/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6244 - loss: 0.8804

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6250 - loss: 0.8754

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6255 - loss: 0.8714

 50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6259 - loss: 0.8682

 55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6264 - loss: 0.8660

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6271 - loss: 0.8639

 65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6279 - loss: 0.8624

 70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6284 - loss: 0.8613

 75/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6285 - loss: 0.8607

 80/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6286 - loss: 0.8601

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6288 - loss: 0.8594

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6292 - loss: 0.8585

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6295 - loss: 0.8578

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6297 - loss: 0.8575

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6300 - loss: 0.8570

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6303 - loss: 0.8563

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6304 - loss: 0.8558

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6304 - loss: 0.8556

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6305 - loss: 0.8553

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6306 - loss: 0.8550

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6307 - loss: 0.8547

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6310 - loss: 0.8542

140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6311 - loss: 0.8539

145/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6313 - loss: 0.8535

149/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6313 - loss: 0.8532

154/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6314 - loss: 0.8529

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6314 - loss: 0.8528

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6314 - loss: 0.8527

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.6315 - loss: 0.8526

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6315 - loss: 0.8526

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6315 - loss: 0.8525

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8524

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8523

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8522

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8521

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8520

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8519

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6314 - loss: 0.8519

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8518

223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8517

228/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6313 - loss: 0.8517

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6313 - loss: 0.8516

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8515

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8514

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8514

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6314 - loss: 0.8513

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6315 - loss: 0.8512

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6315 - loss: 0.8512

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6316 - loss: 0.8511

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6316 - loss: 0.8511

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6317 - loss: 0.8510

304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6317 - loss: 0.8510

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8509

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6318 - loss: 0.8508

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8507

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6319 - loss: 0.8506

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6320 - loss: 0.8505

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6321 - loss: 0.8504

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6322 - loss: 0.8503

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6323 - loss: 0.8502

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6324 - loss: 0.8501

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6324 - loss: 0.8500

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Epoch 30: val_accuracy improved from 0.71167 to 0.71529, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6346 - loss: 0.8485 - val_accuracy: 0.7153 - val_loss: 0.7097 - learning_rate: 0.0010
Epoch 31/45
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Epoch 31: val_accuracy improved from 0.71529 to 0.72072, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6445 - loss: 0.8358 - val_accuracy: 0.7207 - val_loss: 0.6981 - learning_rate: 0.0010
Epoch 32/45
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Epoch 32: val_accuracy did not improve from 0.72072

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6523 - loss: 0.8345 - val_accuracy: 0.6904 - val_loss: 0.7463 - learning_rate: 0.0010
Epoch 33/45
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 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6381 - loss: 0.8331

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6383 - loss: 0.8333

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6386 - loss: 0.8334

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6388 - loss: 0.8336

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6391 - loss: 0.8338

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6394 - loss: 0.8340

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6396 - loss: 0.8342

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6398 - loss: 0.8343

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6400 - loss: 0.8344

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6402 - loss: 0.8344

135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6405 - loss: 0.8344

140/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6406 - loss: 0.8344

145/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6407 - loss: 0.8345

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8348

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8351

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6409 - loss: 0.8354

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8357

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6408 - loss: 0.8360

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6407 - loss: 0.8364

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6406 - loss: 0.8367

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6405 - loss: 0.8370

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6404 - loss: 0.8372

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6403 - loss: 0.8375

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6403 - loss: 0.8377

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6402 - loss: 0.8378

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6402 - loss: 0.8379

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6401 - loss: 0.8381

220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6401 - loss: 0.8382

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8383

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8385

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8386

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8387

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8388

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8389

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6400 - loss: 0.8390

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8391

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8391

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6401 - loss: 0.8392

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6402 - loss: 0.8392

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6402 - loss: 0.8392

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6403 - loss: 0.8392

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6404 - loss: 0.8392

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6404 - loss: 0.8392

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6405 - loss: 0.8392

304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6406 - loss: 0.8392

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6407 - loss: 0.8391

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6408 - loss: 0.8391

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6408 - loss: 0.8391

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6409 - loss: 0.8391

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6410 - loss: 0.8391

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6411 - loss: 0.8390

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6412 - loss: 0.8390

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6413 - loss: 0.8389

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6413 - loss: 0.8389

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6414 - loss: 0.8389

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6415 - loss: 0.8389

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6415 - loss: 0.8389

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6416 - loss: 0.8388

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6417 - loss: 0.8388

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6418 - loss: 0.8387

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6418 - loss: 0.8387

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6419 - loss: 0.8386

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6419 - loss: 0.8386

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6420 - loss: 0.8386

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6420 - loss: 0.8385

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6421 - loss: 0.8385

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6421 - loss: 0.8384

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6422 - loss: 0.8384

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6422 - loss: 0.8383

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6423 - loss: 0.8383

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6423 - loss: 0.8382

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8381

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8381

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6424 - loss: 0.8380

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6425 - loss: 0.8380

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6425 - loss: 0.8379

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6426 - loss: 0.8379
Epoch 33: val_accuracy did not improve from 0.72072

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6426 - loss: 0.8378 - val_accuracy: 0.7147 - val_loss: 0.7017 - learning_rate: 0.0010
Epoch 34/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6250 - loss: 0.8767

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5691 - loss: 0.9941  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5837 - loss: 0.9442

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5961 - loss: 0.9057

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6051 - loss: 0.8858

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6109 - loss: 0.8747

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6158 - loss: 0.8676

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6186 - loss: 0.8632

 42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6213 - loss: 0.8595

 47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6229 - loss: 0.8571

 52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6243 - loss: 0.8545

 57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6258 - loss: 0.8523

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6269 - loss: 0.8511

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6279 - loss: 0.8500

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6290 - loss: 0.8490

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6299 - loss: 0.8483

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6308 - loss: 0.8476

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6317 - loss: 0.8466

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6324 - loss: 0.8456

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6332 - loss: 0.8446

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6340 - loss: 0.8436

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6346 - loss: 0.8427

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6352 - loss: 0.8417

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6357 - loss: 0.8409

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6362 - loss: 0.8402

127/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6365 - loss: 0.8396

132/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6368 - loss: 0.8393

137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6371 - loss: 0.8388

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6375 - loss: 0.8383

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6378 - loss: 0.8378

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6381 - loss: 0.8373

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6384 - loss: 0.8368

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6387 - loss: 0.8363

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6390 - loss: 0.8359

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6393 - loss: 0.8355

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6395 - loss: 0.8352

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6397 - loss: 0.8349

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6399 - loss: 0.8346

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6401 - loss: 0.8343

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6403 - loss: 0.8339

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6405 - loss: 0.8337

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6406 - loss: 0.8334

212/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6408 - loss: 0.8331

217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6410 - loss: 0.8329

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6411 - loss: 0.8326

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6412 - loss: 0.8324

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6414 - loss: 0.8321

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6415 - loss: 0.8318

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6417 - loss: 0.8315

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6418 - loss: 0.8313

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6420 - loss: 0.8310

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6421 - loss: 0.8308

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6422 - loss: 0.8306

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6423 - loss: 0.8305

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6424 - loss: 0.8304

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6425 - loss: 0.8303

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6425 - loss: 0.8301

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6426 - loss: 0.8300

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6427 - loss: 0.8299

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6428 - loss: 0.8297

302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6429 - loss: 0.8296

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6430 - loss: 0.8295

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6430 - loss: 0.8295

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6431 - loss: 0.8294

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6432 - loss: 0.8293

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6433 - loss: 0.8292

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6434 - loss: 0.8291

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6434 - loss: 0.8290

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6435 - loss: 0.8290

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6436 - loss: 0.8290

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6437 - loss: 0.8290

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6437 - loss: 0.8289

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6438 - loss: 0.8289

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6439 - loss: 0.8289

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6439 - loss: 0.8288

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6440 - loss: 0.8288

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6441 - loss: 0.8287

387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6442 - loss: 0.8287

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6442 - loss: 0.8286

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6443 - loss: 0.8286

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6444 - loss: 0.8285

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6444 - loss: 0.8285

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6445 - loss: 0.8284

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6445 - loss: 0.8284

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6446 - loss: 0.8284

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6447 - loss: 0.8284

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8284

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8284

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6448 - loss: 0.8285
Epoch 34: val_accuracy did not improve from 0.72072

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6448 - loss: 0.8285 - val_accuracy: 0.7193 - val_loss: 0.6982 - learning_rate: 0.0010
Epoch 35/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.5938 - loss: 0.9907

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6404 - loss: 0.8769  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6631 - loss: 0.8377

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6712 - loss: 0.8170

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6741 - loss: 0.8077

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6739 - loss: 0.8057

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6734 - loss: 0.8051

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6738 - loss: 0.8032

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6741 - loss: 0.8022

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6739 - loss: 0.8017

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6731 - loss: 0.8019

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6722 - loss: 0.8025

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6715 - loss: 0.8027

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6707 - loss: 0.8030

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6698 - loss: 0.8034

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6692 - loss: 0.8036

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6687 - loss: 0.8035

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6683 - loss: 0.8035

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.8035

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6677 - loss: 0.8036

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6675 - loss: 0.8034

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6674 - loss: 0.8032

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6673 - loss: 0.8032

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6670 - loss: 0.8035

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6668 - loss: 0.8037

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6666 - loss: 0.8040

129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6663 - loss: 0.8044

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6660 - loss: 0.8049

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6657 - loss: 0.8053

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6656 - loss: 0.8057

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.8059

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6654 - loss: 0.8060

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.8062

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.8064

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.8065

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.8067

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.8069

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.8070

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.8072

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6640 - loss: 0.8074

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6638 - loss: 0.8076

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6636 - loss: 0.8078

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6634 - loss: 0.8080

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6632 - loss: 0.8083

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6630 - loss: 0.8086

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6628 - loss: 0.8088

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6627 - loss: 0.8090

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6625 - loss: 0.8092

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6624 - loss: 0.8094

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6622 - loss: 0.8096

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6621 - loss: 0.8097

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6619 - loss: 0.8099

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6618 - loss: 0.8100

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6617 - loss: 0.8103

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6615 - loss: 0.8105

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6614 - loss: 0.8107

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6612 - loss: 0.8109

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6611 - loss: 0.8111

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6610 - loss: 0.8114

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6608 - loss: 0.8117

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6607 - loss: 0.8119

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6605 - loss: 0.8122

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6604 - loss: 0.8124

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6602 - loss: 0.8127

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6601 - loss: 0.8129

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6599 - loss: 0.8131

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6598 - loss: 0.8134

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6596 - loss: 0.8136

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6595 - loss: 0.8138

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6594 - loss: 0.8140

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6593 - loss: 0.8141

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6592 - loss: 0.8142

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6592 - loss: 0.8144

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6591 - loss: 0.8145

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6590 - loss: 0.8146

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Epoch 35: val_accuracy improved from 0.72072 to 0.72493, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6576 - loss: 0.8174 - val_accuracy: 0.7249 - val_loss: 0.6951 - learning_rate: 0.0010
Epoch 36/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6875 - loss: 0.9339

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 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6624 - loss: 0.8614

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 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6600 - loss: 0.8561

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235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6514 - loss: 0.8305

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285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6517 - loss: 0.8296

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314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6519 - loss: 0.8294

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6519 - loss: 0.8293

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419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8284

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429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8284

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8283

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6525 - loss: 0.8283

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8283

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8282

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6526 - loss: 0.8282

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8282

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6527 - loss: 0.8281
Epoch 36: val_accuracy did not improve from 0.72493

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6528 - loss: 0.8280 - val_accuracy: 0.7127 - val_loss: 0.7183 - learning_rate: 0.0010
Epoch 37/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 108ms/step - accuracy: 0.5938 - loss: 0.8450

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5813 - loss: 0.8829  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.5900 - loss: 0.8712

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Epoch 37: val_accuracy improved from 0.72493 to 0.72654, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

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Epoch 38/45
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Epoch 38: val_accuracy improved from 0.72654 to 0.72835, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6485 - loss: 0.8152 - val_accuracy: 0.7284 - val_loss: 0.6847 - learning_rate: 0.0010
Epoch 39/45
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270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6672 - loss: 0.8041

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6671 - loss: 0.8042

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6670 - loss: 0.8043

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6669 - loss: 0.8043

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6669 - loss: 0.8044

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6668 - loss: 0.8045

300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6667 - loss: 0.8046

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6667 - loss: 0.8046

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6666 - loss: 0.8047

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6665 - loss: 0.8048

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6665 - loss: 0.8048

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6664 - loss: 0.8049

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6664 - loss: 0.8050

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6663 - loss: 0.8050

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6663 - loss: 0.8051

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6662 - loss: 0.8051

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8052

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8052

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8053

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6661 - loss: 0.8053

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6660 - loss: 0.8053

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8053

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6659 - loss: 0.8052

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6658 - loss: 0.8053

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6657 - loss: 0.8053

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8053

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8053

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8054

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8054

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6655 - loss: 0.8054

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6655 - loss: 0.8054
Epoch 39: val_accuracy did not improve from 0.72835

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6654 - loss: 0.8055 - val_accuracy: 0.7121 - val_loss: 0.6972 - learning_rate: 0.0010
Epoch 40/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 46s 99ms/step - accuracy: 0.7188 - loss: 0.8825

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6578 - loss: 0.8561 

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6516 - loss: 0.8394

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.8269

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6595 - loss: 0.8211

 24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6588 - loss: 0.8213

 29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.8202

 34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6569 - loss: 0.8177

 39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6555 - loss: 0.8164

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6548 - loss: 0.8146

 49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6546 - loss: 0.8133

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6544 - loss: 0.8124

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6541 - loss: 0.8121

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8112

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6537 - loss: 0.8108

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6536 - loss: 0.8102

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6537 - loss: 0.8095

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6538 - loss: 0.8088

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8082

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6540 - loss: 0.8075

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6541 - loss: 0.8068

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6544 - loss: 0.8060

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6547 - loss: 0.8053

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6550 - loss: 0.8045

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6554 - loss: 0.8037

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6555 - loss: 0.8032

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6556 - loss: 0.8028

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6557 - loss: 0.8024

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6558 - loss: 0.8021

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6560 - loss: 0.8019

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6561 - loss: 0.8017

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6562 - loss: 0.8014

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6563 - loss: 0.8012

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6564 - loss: 0.8011

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6564 - loss: 0.8009

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8009

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8009

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8010

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8010

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6565 - loss: 0.8011

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8011

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8012

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8013

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6566 - loss: 0.8014

217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8014

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8014

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8015

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8015

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8016

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8016

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8017

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8018

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8019

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8019

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8019

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8020

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6572 - loss: 0.8020

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8020

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8020

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8020

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8021

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6575 - loss: 0.8022

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8023

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8024

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8025

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8027

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8028

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8029

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8030

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8032

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8033

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6575 - loss: 0.8034

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8035

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8036

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6576 - loss: 0.8037

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6577 - loss: 0.8038

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6577 - loss: 0.8038

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6578 - loss: 0.8039

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6578 - loss: 0.8040

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6579 - loss: 0.8040

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6579 - loss: 0.8040

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Epoch 40: val_accuracy improved from 0.72835 to 0.73076, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6584 - loss: 0.8050 - val_accuracy: 0.7308 - val_loss: 0.6777 - learning_rate: 0.0010
Epoch 41/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.7281

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Epoch 41: val_accuracy did not improve from 0.73076

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6632 - loss: 0.7969 - val_accuracy: 0.7298 - val_loss: 0.6751 - learning_rate: 0.0010
Epoch 42/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 107ms/step - accuracy: 0.5625 - loss: 0.7943

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Epoch 42: val_accuracy improved from 0.73076 to 0.73398, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_1_20240411-000412.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6653 - loss: 0.8023 - val_accuracy: 0.7340 - val_loss: 0.6685 - learning_rate: 0.0010
Epoch 43/45
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166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6645 - loss: 0.7942

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.7939

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.7937

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6649 - loss: 0.7936

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.7936

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.7936

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.7937

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6652 - loss: 0.7938

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.7938

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.7939

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6654 - loss: 0.7939

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.7939

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6655 - loss: 0.7939

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6656 - loss: 0.7939

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7939

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6658 - loss: 0.7939

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7939

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7939

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7939

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7939

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7940

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7940

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7941

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7942

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7943

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7944

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7944

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7946

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7947

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6660 - loss: 0.7948

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6660 - loss: 0.7949

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7949

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7950

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7951

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6659 - loss: 0.7952

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7952

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7953

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7954

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7954

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6658 - loss: 0.7955

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7956

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7957

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6657 - loss: 0.7958

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7958

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7959

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7959

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6656 - loss: 0.7960

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6655 - loss: 0.7961

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7961

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7962

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.7962

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7963

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7963

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7964

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.7964

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7965

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7965

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6653 - loss: 0.7966

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7967

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7967

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7968

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7968

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6652 - loss: 0.7969
Epoch 43: val_accuracy did not improve from 0.73398

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6651 - loss: 0.7971 - val_accuracy: 0.7205 - val_loss: 0.7075 - learning_rate: 0.0010
Epoch 44/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5625 - loss: 0.7509

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6342 - loss: 0.7526  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6516 - loss: 0.7573

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6455 - loss: 0.7804

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6449 - loss: 0.7869

 24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6462 - loss: 0.7888

 28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6488 - loss: 0.7882

 32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6515 - loss: 0.7868

 35/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6536 - loss: 0.7853

 39/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6565 - loss: 0.7828

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.6593 - loss: 0.7799

 49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6610 - loss: 0.7782

 54/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6623 - loss: 0.7775

 59/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6634 - loss: 0.7769

 64/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6643 - loss: 0.7761

 69/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6654 - loss: 0.7752

 74/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6662 - loss: 0.7745

 79/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6668 - loss: 0.7741

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6672 - loss: 0.7737

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.6676 - loss: 0.7736

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6679 - loss: 0.7737

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7738

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7740

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7744

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7746

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7748

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6682 - loss: 0.7751

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7755

133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6681 - loss: 0.7759

138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.7762

143/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6680 - loss: 0.7766

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6679 - loss: 0.7769

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6678 - loss: 0.7772

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6678 - loss: 0.7776

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6677 - loss: 0.7779

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6676 - loss: 0.7781

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6676 - loss: 0.7784

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6675 - loss: 0.7786

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6675 - loss: 0.7788

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6674 - loss: 0.7791

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6673 - loss: 0.7794

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6672 - loss: 0.7796

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6671 - loss: 0.7799

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6670 - loss: 0.7802

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6669 - loss: 0.7805

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6668 - loss: 0.7807

223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6668 - loss: 0.7809

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6667 - loss: 0.7811

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6667 - loss: 0.7813

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7815

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7817

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7819

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7821

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7823

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7825

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7827

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7829

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7830

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7832

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7833

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7834

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7835

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7836

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7837

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7838

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7839

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7840

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7841

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7843

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7844

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7846

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7847

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7849

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7851

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7853

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7855

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7857

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7859

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.7861

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.7863

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7865

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7867

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6664 - loss: 0.7868

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7869

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7871

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7873

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7874

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7876

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7877

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6663 - loss: 0.7879

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7880

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7882

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7883

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7884

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7885

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.7886
Epoch 44: val_accuracy did not improve from 0.73398

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6662 - loss: 0.7888 - val_accuracy: 0.7304 - val_loss: 0.6849 - learning_rate: 0.0010
Epoch 45/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.5312 - loss: 0.9538

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6024 - loss: 0.8991  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6321 - loss: 0.8606

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6435 - loss: 0.8421

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6508 - loss: 0.8284

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6562 - loss: 0.8174

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6599 - loss: 0.8101

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6619 - loss: 0.8050

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6638 - loss: 0.8012

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6648 - loss: 0.7987

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.7971

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6657 - loss: 0.7958

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6656 - loss: 0.7952

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6655 - loss: 0.7947

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.7940

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6653 - loss: 0.7935

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6651 - loss: 0.7933

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6649 - loss: 0.7934

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6647 - loss: 0.7935

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6645 - loss: 0.7936

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6644 - loss: 0.7937

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6642 - loss: 0.7938

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7940

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7941

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7940

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6642 - loss: 0.7940

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.7940

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7939

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7940

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7941

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6643 - loss: 0.7941

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.7942

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6645 - loss: 0.7941

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.7940

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6647 - loss: 0.7940

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.7939

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6650 - loss: 0.7939

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.7939

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6652 - loss: 0.7938

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6654 - loss: 0.7937

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6655 - loss: 0.7936

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6656 - loss: 0.7935

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6657 - loss: 0.7934

214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7933

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6658 - loss: 0.7932

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7931

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6659 - loss: 0.7930

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7929

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6661 - loss: 0.7928

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7927

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7926

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7925

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7925

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7924

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7924

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7923

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6663 - loss: 0.7923

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7922

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7922

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7921

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6665 - loss: 0.7920

302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7919

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7919

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7918

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.7917

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7916

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7915

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.7915

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7914

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7914

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.7913

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7913

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7913

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.7912

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6668 - loss: 0.7912

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6668 - loss: 0.7912

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6669 - loss: 0.7912

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434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7912

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453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6670 - loss: 0.7912

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6671 - loss: 0.7912

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6671 - loss: 0.7912
Epoch 45: val_accuracy did not improve from 0.73398

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6671 - loss: 0.7912 - val_accuracy: 0.7284 - val_loss: 0.6867 - learning_rate: 0.0010
Restoring model weights from the end of the best epoch: 42.

Plotting the Training and Validation Accuracies¶

In [22]:
plt.plot(history_1.history["accuracy"])
plt.plot(history_1.history["val_accuracy"])
plt.title("CNN Model 1 accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the Model on the Test Set¶

In [23]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = model_1.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.8438 - loss: 0.4053

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7573 - loss: 0.5913 
Loss: 0.65003901720047, Accuracy: 0.734375

Plotting Confusion Matrix¶

In [24]:
pred_probabilities = model_1.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("CNN Model 1 Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 365ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step  
              precision    recall  f1-score   support

       happy       0.84      0.84      0.84        32
     neutral       0.65      0.69      0.67        32
         sad       0.54      0.59      0.57        32
    surprise       0.96      0.81      0.88        32

    accuracy                           0.73       128
   macro avg       0.75      0.73      0.74       128
weighted avg       0.75      0.73      0.74       128

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Observations and Insights:

  • The model has a substantial number of 651,780 trainable parameters, suggesting a complex architecture capable of learning detailed features.
  • The model's performance on the test set shows an accuracy of 73.44%.
  • Regarding the confusion matrix, the model identifies the 'surprise' emotion with an f1-score of 0.88, and performs also well with 'happy' at an f1-score of 0.84. However, 'neutral' and 'sad' emotions have lower f1-scores of 0.67 and 0.57 respectively, suggesting the model's difficulty in distinguishing these emotions as accurately.
  • The f1-score, which balances precision and recall, suggests that 'sad' is the most challenging emotion for the model to predict correctly.

Creating the second Convolutional Neural Network¶

Model 2 Architecture:¶

  • This model is designed with a sequential structure, incorporating four convolutional blocks for feature extraction, followed by dense layers for classification.

  • First Convolutional Block:

    • Begins with a Conv2D layer with 256 filters, a 2x2 kernel size, 'same' padding, and an input shape of (48, 48, 1), indicating grayscale images of size 48x48.
    • Includes BatchNormalization to stabilize and speed up training.
    • Utilizes LeakyReLU with a negative slope of 0.1 for activation, allowing a small gradient when the unit is not active.
    • Applies MaxPooling2D with a pool size of 2 to reduce spatial dimensions.
  • Second Convolutional Block:

    • Consists of a Conv2D layer with 128 filters and a 2x2 kernel size, using 'same' padding.
    • Follows the same pattern of BatchNormalization, LeakyReLU activation, and MaxPooling2D.
  • Third Convolutional Block:

    • Features a Conv2D layer with 64 filters and a 2x2 kernel size, maintaining 'same' padding.
    • Repeats the BatchNormalization, LeakyReLU activation, and MaxPooling2D structure.
  • Fourth Convolutional Block:

    • Contains a Conv2D layer with 32 filters and a 2x2 kernel size, with 'same' padding.
    • Continues with BatchNormalization, LeakyReLU activation, and MaxPooling2D.
  • After processing through the convolutional blocks, the model flattens the output to transition to fully connected layers.

  • Fully Connected Dense Layers:

    • Incorporates a dense layer with 512 neurons and 'relu' activation.
    • Followed by a dense layer with 128 neurons and 'relu' activation.
    • Then, a dense layer with 64 neurons without an explicit activation is added before BatchNormalization and ReLU activation to introduce non-linearity.
  • Output Layer:

    • Concludes with a Dense layer having 4 neurons and 'softmax' activation for multi-class classification of 4 emotions.
  • The model employs the Adam optimizer with a learning rate of 0.001, optimizing the categorical crossentropy loss function for training.

In [25]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [26]:
# Initializing a sequential model
model_2 = Sequential()

model_2.add(Input(shape=(img_width, img_height, color_layers)))

# First Convolutional Block
model_2.add(Conv2D(256, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))

# Second Convolutional Block
model_2.add(Conv2D(128, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))

# Third Convolutional Block
model_2.add(Conv2D(64, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))

# Fourth Convolutional Block
model_2.add(Conv2D(32, kernel_size=2, padding="same"))
model_2.add(BatchNormalization())
model_2.add(LeakyReLU(negative_slope=0.1))
model_2.add(MaxPooling2D(pool_size=2))

# Flatten the output of the conv layers to feed into the dense layers
model_2.add(Flatten())

# Fully connected layers
model_2.add(Dense(512, activation="relu"))
model_2.add(Dense(128, activation="relu"))
model_2.add(Dense(64))
model_2.add(BatchNormalization())
model_2.add(ReLU())  # Using ReLU after batch normalization

# Adding output layer
model_2.add(Dense(4, activation="softmax"))

# Using Adam Optimizer
optimizer = Adam(learning_rate=0.001)

Compiling and Training the Model¶

In [27]:
# Compile the model
model_2.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])

model_2.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 48, 48, 256)    │         1,280 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 48, 48, 256)    │         1,024 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu (LeakyReLU)         │ (None, 48, 48, 256)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 24, 24, 256)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 24, 24, 128)    │       131,200 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 24, 24, 128)    │           512 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_1 (LeakyReLU)       │ (None, 24, 24, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 12, 12, 64)     │        32,832 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_2           │ (None, 12, 12, 64)     │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_2 (LeakyReLU)       │ (None, 12, 12, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_2 (MaxPooling2D)  │ (None, 6, 6, 64)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_3 (Conv2D)               │ (None, 6, 6, 32)       │         8,224 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_3           │ (None, 6, 6, 32)       │           128 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_3 (LeakyReLU)       │ (None, 6, 6, 32)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_3 (MaxPooling2D)  │ (None, 3, 3, 32)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 288)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 512)            │       147,968 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 128)            │        65,664 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 64)             │         8,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_4           │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu (ReLU)                    │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 397,860 (1.52 MB)
 Trainable params: 396,772 (1.51 MB)
 Non-trainable params: 1,088 (4.25 KB)
In [28]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

# Define the saving the best model callback
mc = ModelCheckpoint(
    f"{results_path}/best_model_2_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 45 epochs and using validation set
history_2 = model_2.fit(
    train_generator,
    epochs=45,
    validation_data=validation_generator,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/45
I0000 00:00:1712794150.228648 1482664 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_2631', 4 bytes spill stores, 4 bytes spill loads

  1/473 ━━━━━━━━━━━━━━━━━━━━ 42:28 5s/step - accuracy: 0.3438 - loss: 1.3840

  5/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.2798 - loss: 1.6040 

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 13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.2964 - loss: 1.6224

 18/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.2983 - loss: 1.6201

 22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.2994 - loss: 1.6173

 26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.3013 - loss: 1.6110

 31/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.3042 - loss: 1.6004

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 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3075 - loss: 1.5808

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3088 - loss: 1.5720

 50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3097 - loss: 1.5655

 55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3109 - loss: 1.5576

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3122 - loss: 1.5498

 65/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3135 - loss: 1.5425

 70/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.3149 - loss: 1.5358

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101/473 ━━━━━━━━━━━━━━━━━━━━ 15s 42ms/step - accuracy: 0.3215 - loss: 1.5019

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165/473 ━━━━━━━━━━━━━━━━━━━━ 9s 30ms/step - accuracy: 0.3316 - loss: 1.4589

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Epoch 1: val_accuracy improved from -inf to 0.38798, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 17s 24ms/step - accuracy: 0.3616 - loss: 1.3675 - val_accuracy: 0.3880 - val_loss: 1.2428 - learning_rate: 0.0010
Epoch 2/45
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Epoch 2: val_accuracy improved from 0.38798 to 0.47197, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4843 - loss: 1.1404 - val_accuracy: 0.4720 - val_loss: 1.3282 - learning_rate: 0.0010
Epoch 3/45
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Epoch 3: val_accuracy improved from 0.47197 to 0.52863, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5619 - loss: 1.0254 - val_accuracy: 0.5286 - val_loss: 1.1775 - learning_rate: 0.0010
Epoch 4/45
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Epoch 4: val_accuracy improved from 0.52863 to 0.65702, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5950 - loss: 0.9466 - val_accuracy: 0.6570 - val_loss: 0.8246 - learning_rate: 0.0010
Epoch 5/45
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 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5940 - loss: 0.9336

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5951 - loss: 0.9318

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5961 - loss: 0.9301

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5969 - loss: 0.9288

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5975 - loss: 0.9279

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5978 - loss: 0.9271

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5982 - loss: 0.9261

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5986 - loss: 0.9252

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5990 - loss: 0.9242

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5996 - loss: 0.9233

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6001 - loss: 0.9224

126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6005 - loss: 0.9217

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6010 - loss: 0.9211

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6013 - loss: 0.9206

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6017 - loss: 0.9200

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6021 - loss: 0.9194

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6024 - loss: 0.9189

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6028 - loss: 0.9184

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6030 - loss: 0.9180

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6032 - loss: 0.9176

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6035 - loss: 0.9172

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6037 - loss: 0.9169

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6039 - loss: 0.9166

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6041 - loss: 0.9164

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6043 - loss: 0.9162

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6045 - loss: 0.9160

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6047 - loss: 0.9158

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6049 - loss: 0.9156

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6051 - loss: 0.9155

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6052 - loss: 0.9155

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6053 - loss: 0.9154

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6054 - loss: 0.9155

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6054 - loss: 0.9155

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6055 - loss: 0.9155

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6056 - loss: 0.9155

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6056 - loss: 0.9155

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6057 - loss: 0.9154

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6058 - loss: 0.9154

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6059 - loss: 0.9153

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6060 - loss: 0.9152

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6061 - loss: 0.9151

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6062 - loss: 0.9150

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6062 - loss: 0.9149

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6063 - loss: 0.9148

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6063 - loss: 0.9147

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6064 - loss: 0.9147

301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6064 - loss: 0.9146

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6065 - loss: 0.9146

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9145

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9144

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6066 - loss: 0.9143

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9143

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9142

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6067 - loss: 0.9142

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9141

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9141

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6068 - loss: 0.9140

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6069 - loss: 0.9140

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6069 - loss: 0.9139

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6070 - loss: 0.9138

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6070 - loss: 0.9137

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6071 - loss: 0.9136

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6072 - loss: 0.9134

387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6072 - loss: 0.9133

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6073 - loss: 0.9132

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6074 - loss: 0.9131

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6074 - loss: 0.9130

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6075 - loss: 0.9129

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6075 - loss: 0.9128

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6076 - loss: 0.9127

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6076 - loss: 0.9127

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6077 - loss: 0.9126

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6077 - loss: 0.9125

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6078 - loss: 0.9125

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6078 - loss: 0.9124

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6079 - loss: 0.9123

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6079 - loss: 0.9122

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6080 - loss: 0.9121

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6081 - loss: 0.9120

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6081 - loss: 0.9119
Epoch 5: val_accuracy did not improve from 0.65702

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6082 - loss: 0.9118 - val_accuracy: 0.6319 - val_loss: 0.8880 - learning_rate: 0.0010
Epoch 6/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 51s 108ms/step - accuracy: 0.5938 - loss: 0.8551

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5984 - loss: 0.8713  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6135 - loss: 0.8580

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6202 - loss: 0.8558

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6244 - loss: 0.8547

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6284 - loss: 0.8522

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6290 - loss: 0.8541

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6283 - loss: 0.8576

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6276 - loss: 0.8608

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6266 - loss: 0.8639

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6259 - loss: 0.8659

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6259 - loss: 0.8669

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6255 - loss: 0.8685

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6252 - loss: 0.8700

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6251 - loss: 0.8712

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6253 - loss: 0.8717

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6256 - loss: 0.8719

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6259 - loss: 0.8719

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6263 - loss: 0.8718

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6267 - loss: 0.8717

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6269 - loss: 0.8716

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6271 - loss: 0.8716

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6273 - loss: 0.8716

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6274 - loss: 0.8716

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6275 - loss: 0.8717

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6276 - loss: 0.8718

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8717

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8717

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6276 - loss: 0.8719

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6277 - loss: 0.8719

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8720

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8720

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8722

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8723

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6279 - loss: 0.8725

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6279 - loss: 0.8727

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8730

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8732

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8733

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6278 - loss: 0.8735

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Epoch 6: val_accuracy improved from 0.65702 to 0.66024, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6291 - loss: 0.8715 - val_accuracy: 0.6602 - val_loss: 0.8272 - learning_rate: 0.0010
Epoch 7/45
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Epoch 7: val_accuracy improved from 0.66024 to 0.68957, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6362 - loss: 0.8523 - val_accuracy: 0.6896 - val_loss: 0.7543 - learning_rate: 0.0010
Epoch 8/45
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419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6478 - loss: 0.8314

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6477 - loss: 0.8316

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6476 - loss: 0.8317

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6475 - loss: 0.8319

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6475 - loss: 0.8320

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6474 - loss: 0.8321

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6474 - loss: 0.8322

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6473 - loss: 0.8323

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6473 - loss: 0.8324

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6472 - loss: 0.8324

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6472 - loss: 0.8325
Epoch 8: val_accuracy did not improve from 0.68957

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6471 - loss: 0.8326 - val_accuracy: 0.6637 - val_loss: 0.8172 - learning_rate: 0.0010
Epoch 9/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.7500 - loss: 0.7112

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7065 - loss: 0.7224  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6903 - loss: 0.7391

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6803 - loss: 0.7517

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6765 - loss: 0.7606

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6752 - loss: 0.7673

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6735 - loss: 0.7736

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6713 - loss: 0.7792

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6691 - loss: 0.7836

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6677 - loss: 0.7867

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6665 - loss: 0.7891

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6656 - loss: 0.7912

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6648 - loss: 0.7932

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6641 - loss: 0.7950

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6633 - loss: 0.7974

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6626 - loss: 0.7993

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6622 - loss: 0.8008

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6621 - loss: 0.8017

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6620 - loss: 0.8026

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6619 - loss: 0.8039

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6618 - loss: 0.8050

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6616 - loss: 0.8062

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6614 - loss: 0.8073

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6611 - loss: 0.8083

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6608 - loss: 0.8095

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6605 - loss: 0.8106

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6602 - loss: 0.8117

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6600 - loss: 0.8127

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6598 - loss: 0.8137

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8144

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6594 - loss: 0.8152

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8159

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8165

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6591 - loss: 0.8170

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8176

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8181

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6589 - loss: 0.8187

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8191

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6587 - loss: 0.8197

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6586 - loss: 0.8201

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6584 - loss: 0.8207

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6582 - loss: 0.8212

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6581 - loss: 0.8217

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6580 - loss: 0.8221

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6579 - loss: 0.8225

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6578 - loss: 0.8228

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6577 - loss: 0.8231

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6576 - loss: 0.8235

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6575 - loss: 0.8238

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8241

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6574 - loss: 0.8244

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6572 - loss: 0.8247

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8250

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6570 - loss: 0.8253

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6569 - loss: 0.8256

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8259

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6567 - loss: 0.8261

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6566 - loss: 0.8264

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6565 - loss: 0.8266

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6564 - loss: 0.8268

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6564 - loss: 0.8270

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6563 - loss: 0.8272

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6562 - loss: 0.8274

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6561 - loss: 0.8276

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6560 - loss: 0.8278

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6559 - loss: 0.8280

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6558 - loss: 0.8282

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6557 - loss: 0.8284

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6556 - loss: 0.8285

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6555 - loss: 0.8287

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6554 - loss: 0.8289

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6553 - loss: 0.8290

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6553 - loss: 0.8292

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6552 - loss: 0.8293

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6551 - loss: 0.8295

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6550 - loss: 0.8296

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6549 - loss: 0.8297

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6549 - loss: 0.8298

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6548 - loss: 0.8299

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6547 - loss: 0.8300

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6547 - loss: 0.8300

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6546 - loss: 0.8301

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6546 - loss: 0.8302

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6545 - loss: 0.8303

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6544 - loss: 0.8304

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6544 - loss: 0.8304

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6543 - loss: 0.8305

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6543 - loss: 0.8306

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6542 - loss: 0.8306

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6542 - loss: 0.8307

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8307

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8308

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6541 - loss: 0.8309

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6540 - loss: 0.8309

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6540 - loss: 0.8310

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6539 - loss: 0.8310

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6539 - loss: 0.8311
Epoch 9: val_accuracy did not improve from 0.68957

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6539 - loss: 0.8311 - val_accuracy: 0.6323 - val_loss: 0.8551 - learning_rate: 0.0010
Epoch 10/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6250 - loss: 1.0567

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6631 - loss: 0.9727  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6865 - loss: 0.8890

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6972 - loss: 0.8538

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6997 - loss: 0.8367

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6991 - loss: 0.8268

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6977 - loss: 0.8203

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6964 - loss: 0.8152

 39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6949 - loss: 0.8121

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6938 - loss: 0.8083

 49/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6925 - loss: 0.8061

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6911 - loss: 0.8042

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6897 - loss: 0.8031

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6885 - loss: 0.8020

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6876 - loss: 0.8008

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6868 - loss: 0.8007

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6859 - loss: 0.8011

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6849 - loss: 0.8020

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6838 - loss: 0.8029

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6829 - loss: 0.8036

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6819 - loss: 0.8042

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6809 - loss: 0.8050

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6801 - loss: 0.8057

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6794 - loss: 0.8062

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6788 - loss: 0.8067

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6783 - loss: 0.8071

129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6778 - loss: 0.8076

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6772 - loss: 0.8082

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6766 - loss: 0.8088

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6760 - loss: 0.8093

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6755 - loss: 0.8098

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6750 - loss: 0.8103

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6746 - loss: 0.8107

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6742 - loss: 0.8110

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6739 - loss: 0.8114

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6735 - loss: 0.8117

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6732 - loss: 0.8119

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6728 - loss: 0.8122

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6725 - loss: 0.8124

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8127

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6718 - loss: 0.8129

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6715 - loss: 0.8131

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6712 - loss: 0.8133

214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6709 - loss: 0.8135

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6706 - loss: 0.8137

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8139

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6700 - loss: 0.8141

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6698 - loss: 0.8143

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6695 - loss: 0.8144

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6693 - loss: 0.8145

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6692 - loss: 0.8146

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6690 - loss: 0.8146

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6689 - loss: 0.8147

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6687 - loss: 0.8148

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6686 - loss: 0.8148

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6684 - loss: 0.8149

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6683 - loss: 0.8150

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6682 - loss: 0.8150

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6681 - loss: 0.8151

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6679 - loss: 0.8152

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.8152

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.8152

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6677 - loss: 0.8153

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6675 - loss: 0.8153

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6674 - loss: 0.8154

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6673 - loss: 0.8155

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6672 - loss: 0.8156

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6671 - loss: 0.8156

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6670 - loss: 0.8157

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6669 - loss: 0.8158

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6668 - loss: 0.8158

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6667 - loss: 0.8159

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.8159

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6666 - loss: 0.8160

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6665 - loss: 0.8160

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.8160

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6664 - loss: 0.8161

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6663 - loss: 0.8161

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6663 - loss: 0.8161

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.8161

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6662 - loss: 0.8161

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6661 - loss: 0.8160

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6660 - loss: 0.8160

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6660 - loss: 0.8160

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6659 - loss: 0.8160

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6658 - loss: 0.8160

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6658 - loss: 0.8160

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8160

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6657 - loss: 0.8160

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8160

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6656 - loss: 0.8160

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6655 - loss: 0.8160

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.8161

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6654 - loss: 0.8161
Epoch 10: val_accuracy did not improve from 0.68957

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6653 - loss: 0.8162 - val_accuracy: 0.6337 - val_loss: 0.8556 - learning_rate: 0.0010
Epoch 11/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.6875 - loss: 0.7334

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6606 - loss: 0.8048  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6520 - loss: 0.8091

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6517 - loss: 0.8120

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6522 - loss: 0.8124

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6539 - loss: 0.8131

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6545 - loss: 0.8135

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6539 - loss: 0.8165

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6530 - loss: 0.8197

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6525 - loss: 0.8218

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6524 - loss: 0.8229

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6526 - loss: 0.8236

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6532 - loss: 0.8238

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6539 - loss: 0.8235

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6548 - loss: 0.8231

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6556 - loss: 0.8230

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6563 - loss: 0.8226

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6568 - loss: 0.8225

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6571 - loss: 0.8226

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6574 - loss: 0.8227

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6575 - loss: 0.8227

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6578 - loss: 0.8227

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6580 - loss: 0.8228

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6581 - loss: 0.8228

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6583 - loss: 0.8227

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6584 - loss: 0.8225

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6586 - loss: 0.8223

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8220

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6590 - loss: 0.8217

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8215

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6593 - loss: 0.8213

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6595 - loss: 0.8211

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8209

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8206

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8205

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6596 - loss: 0.8204

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Epoch 11: val_accuracy improved from 0.68957 to 0.69781, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6580 - loss: 0.8165 - val_accuracy: 0.6978 - val_loss: 0.7402 - learning_rate: 0.0010
Epoch 12/45
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Epoch 12: val_accuracy improved from 0.69781 to 0.71569, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6732 - loss: 0.7880 - val_accuracy: 0.7157 - val_loss: 0.7013 - learning_rate: 0.0010
Epoch 13/45
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437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6706 - loss: 0.7856

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6706 - loss: 0.7856

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6707 - loss: 0.7856

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6707 - loss: 0.7856

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6708 - loss: 0.7856

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6708 - loss: 0.7856

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6709 - loss: 0.7856
Epoch 13: val_accuracy did not improve from 0.71569

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6709 - loss: 0.7856 - val_accuracy: 0.6835 - val_loss: 0.7741 - learning_rate: 0.0010
Epoch 14/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7188 - loss: 0.9271

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6854 - loss: 0.8450  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6876 - loss: 0.8176

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6870 - loss: 0.8018

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6866 - loss: 0.7947

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6831 - loss: 0.7928

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6808 - loss: 0.7901

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6809 - loss: 0.7869

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6802 - loss: 0.7863

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6795 - loss: 0.7862

 49/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7857

 53/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6781 - loss: 0.7856

 58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6777 - loss: 0.7862

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6774 - loss: 0.7868

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6766 - loss: 0.7879

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6760 - loss: 0.7886

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6754 - loss: 0.7894

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6749 - loss: 0.7900

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7904

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7905

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6746 - loss: 0.7906

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7908

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6743 - loss: 0.7909

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6742 - loss: 0.7909

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6742 - loss: 0.7909

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6743 - loss: 0.7908

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7907

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7906

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6746 - loss: 0.7904

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6747 - loss: 0.7900

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7896

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7893

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7890

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7889

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7888

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7887

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7886

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7885

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7885

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7884

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7884

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7884

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7883

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7883

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7882

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7881

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7880

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7879

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7878

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7877

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6748 - loss: 0.7877

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6747 - loss: 0.7877

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6747 - loss: 0.7877

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6746 - loss: 0.7878

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6746 - loss: 0.7878

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7878

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6745 - loss: 0.7879

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6744 - loss: 0.7880

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6744 - loss: 0.7881

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6743 - loss: 0.7882

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6743 - loss: 0.7883

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7884

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7885

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 0.7885

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7886

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7887

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6741 - loss: 0.7887

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6740 - loss: 0.7888

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7887

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7886

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7886

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7885

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7884

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6740 - loss: 0.7883
Epoch 14: val_accuracy did not improve from 0.71569

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6740 - loss: 0.7883 - val_accuracy: 0.6618 - val_loss: 0.8420 - learning_rate: 0.0010
Epoch 15/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 56s 120ms/step - accuracy: 0.5625 - loss: 0.8298

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6201 - loss: 0.8163  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6340 - loss: 0.8172

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6443 - loss: 0.8162

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6511 - loss: 0.8115

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6546 - loss: 0.8095

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6556 - loss: 0.8088

 34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6558 - loss: 0.8077

 38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6566 - loss: 0.8054

 42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6573 - loss: 0.8032

 47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6576 - loss: 0.8008

 52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6577 - loss: 0.7987

 57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6576 - loss: 0.7968

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6576 - loss: 0.7953

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6575 - loss: 0.7945

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6575 - loss: 0.7937

 78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6576 - loss: 0.7930

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6577 - loss: 0.7926

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6577 - loss: 0.7924

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6578 - loss: 0.7923

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6580 - loss: 0.7923

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6583 - loss: 0.7920

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6586 - loss: 0.7916

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6590 - loss: 0.7912

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6593 - loss: 0.7909

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6596 - loss: 0.7907

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6598 - loss: 0.7905

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6601 - loss: 0.7903

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6605 - loss: 0.7899

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6610 - loss: 0.7894

141/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6614 - loss: 0.7890

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6617 - loss: 0.7886

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6619 - loss: 0.7883

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6622 - loss: 0.7881

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6624 - loss: 0.7879

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6626 - loss: 0.7877

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6629 - loss: 0.7874

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6631 - loss: 0.7871

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6634 - loss: 0.7868

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6637 - loss: 0.7865

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6640 - loss: 0.7861

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6642 - loss: 0.7858

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6644 - loss: 0.7855

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6646 - loss: 0.7853

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6648 - loss: 0.7850

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6651 - loss: 0.7847

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6653 - loss: 0.7844

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6655 - loss: 0.7842

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6657 - loss: 0.7839

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6660 - loss: 0.7836

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6662 - loss: 0.7834

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6664 - loss: 0.7832

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6666 - loss: 0.7830

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6668 - loss: 0.7827

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6670 - loss: 0.7826

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6672 - loss: 0.7825

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6673 - loss: 0.7824

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6675 - loss: 0.7823

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6677 - loss: 0.7822

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6678 - loss: 0.7821

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6680 - loss: 0.7819

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6682 - loss: 0.7818

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6683 - loss: 0.7817

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6685 - loss: 0.7816

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6686 - loss: 0.7815

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6688 - loss: 0.7813

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.7812

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6691 - loss: 0.7811

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.7810

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.7809

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.7809

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6695 - loss: 0.7808

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6696 - loss: 0.7808

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.7808

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.7807

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.7807

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.7807

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.7807

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6700 - loss: 0.7806

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6700 - loss: 0.7806

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7806

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7806

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6701 - loss: 0.7805

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6702 - loss: 0.7805

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6702 - loss: 0.7805

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6703 - loss: 0.7805

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6703 - loss: 0.7804

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6704 - loss: 0.7804

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6704 - loss: 0.7804

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6705 - loss: 0.7804

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6705 - loss: 0.7804

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6706 - loss: 0.7803

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6706 - loss: 0.7803

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6707 - loss: 0.7803

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6707 - loss: 0.7803

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6708 - loss: 0.7803

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6709 - loss: 0.7803
Epoch 15: val_accuracy did not improve from 0.71569

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6709 - loss: 0.7802 - val_accuracy: 0.7113 - val_loss: 0.7071 - learning_rate: 0.0010
Epoch 16/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7188 - loss: 0.8237

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7056 - loss: 0.7728  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6908 - loss: 0.7747

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6835 - loss: 0.7797

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6803 - loss: 0.7816

 24/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6780 - loss: 0.7809

 29/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6750 - loss: 0.7806

 34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6729 - loss: 0.7806

 39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6718 - loss: 0.7805

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6719 - loss: 0.7793

 49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6724 - loss: 0.7782

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6730 - loss: 0.7774

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6737 - loss: 0.7767

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6740 - loss: 0.7766

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6744 - loss: 0.7764

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6747 - loss: 0.7763

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6750 - loss: 0.7762

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6754 - loss: 0.7760

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6757 - loss: 0.7759

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6760 - loss: 0.7756

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6764 - loss: 0.7752

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6766 - loss: 0.7747

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6767 - loss: 0.7745

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6768 - loss: 0.7743

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7742

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7742

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6769 - loss: 0.7744

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6769 - loss: 0.7747

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6768 - loss: 0.7749

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6768 - loss: 0.7750

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7752

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7753

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7754

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7755

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7755

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6765 - loss: 0.7756

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6766 - loss: 0.7756

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7756

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7756

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6767 - loss: 0.7757

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7757

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7757

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7756

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6770 - loss: 0.7756

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6771 - loss: 0.7755

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6772 - loss: 0.7755

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6772 - loss: 0.7755

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6773 - loss: 0.7756

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6774 - loss: 0.7756

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6774 - loss: 0.7756

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7756

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7756

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7755

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6776 - loss: 0.7755

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7755

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7754

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6778 - loss: 0.7754

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6778 - loss: 0.7753

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6779 - loss: 0.7752

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6779 - loss: 0.7752

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6780 - loss: 0.7751

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6781 - loss: 0.7751

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6781 - loss: 0.7750

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6782 - loss: 0.7749

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6782 - loss: 0.7749

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6783 - loss: 0.7748

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6783 - loss: 0.7748

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6784 - loss: 0.7747

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6785 - loss: 0.7746

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6785 - loss: 0.7746

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6785 - loss: 0.7746

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7746

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7746

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6786 - loss: 0.7747
Epoch 16: val_accuracy did not improve from 0.71569

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6786 - loss: 0.7747 - val_accuracy: 0.6803 - val_loss: 0.7580 - learning_rate: 0.0010
Epoch 17/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 102ms/step - accuracy: 0.6875 - loss: 0.9330

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7222 - loss: 0.7754  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7059 - loss: 0.7731

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6981 - loss: 0.7764

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6935 - loss: 0.7768

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6907 - loss: 0.7767

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6884 - loss: 0.7774

 34/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6863 - loss: 0.7788

 39/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6841 - loss: 0.7794

 42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6830 - loss: 0.7798

 47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6817 - loss: 0.7800

 52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6806 - loss: 0.7802

 57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6795 - loss: 0.7805

 62/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6788 - loss: 0.7801

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7793

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7785

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7781

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6785 - loss: 0.7777

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6784 - loss: 0.7775

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6785 - loss: 0.7772

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6786 - loss: 0.7770

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7768

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7767

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7765

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6787 - loss: 0.7762

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6788 - loss: 0.7759

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6788 - loss: 0.7758

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7755

135/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7753

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6789 - loss: 0.7752

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6788 - loss: 0.7752

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7752

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7751

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7750

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6787 - loss: 0.7749

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7749

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7747

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7745

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7744

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6786 - loss: 0.7742

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6785 - loss: 0.7741

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6785 - loss: 0.7740

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6784 - loss: 0.7740

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6783 - loss: 0.7740

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6782 - loss: 0.7740

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6781 - loss: 0.7741

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6780 - loss: 0.7741

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6779 - loss: 0.7741

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6778 - loss: 0.7741

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7740

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6777 - loss: 0.7740

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7740

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7739

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7739

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7738

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7737

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7737

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7736

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7735

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6775 - loss: 0.7734

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7734

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6776 - loss: 0.7733

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6776 - loss: 0.7732

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7731

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7730

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6777 - loss: 0.7729

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7728

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7727

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6778 - loss: 0.7726

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7725

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7724

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7723

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6779 - loss: 0.7722

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6780 - loss: 0.7721

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6780 - loss: 0.7720

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7718

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7718

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6781 - loss: 0.7717

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6782 - loss: 0.7716

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6782 - loss: 0.7715

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6782 - loss: 0.7714

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6782 - loss: 0.7714

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7713

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7712

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6783 - loss: 0.7711

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6784 - loss: 0.7710

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6784 - loss: 0.7709

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6784 - loss: 0.7709

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6784 - loss: 0.7708

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7707

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7707

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7706

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7706

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7706

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7705

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7705

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6785 - loss: 0.7704
Epoch 17: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 17: val_accuracy did not improve from 0.71569

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6785 - loss: 0.7704 - val_accuracy: 0.6797 - val_loss: 0.7835 - learning_rate: 0.0010
Epoch 18/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.6562 - loss: 0.8476

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7261 - loss: 0.7122  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7198 - loss: 0.7154

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7125 - loss: 0.7286

 21/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7075 - loss: 0.7357

 26/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7044 - loss: 0.7396

 31/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7025 - loss: 0.7423

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7011 - loss: 0.7439

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7000 - loss: 0.7463

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6996 - loss: 0.7479

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7002 - loss: 0.7477

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7010 - loss: 0.7470

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7011 - loss: 0.7468

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7012 - loss: 0.7464

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7014 - loss: 0.7458

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7016 - loss: 0.7453

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7017 - loss: 0.7451

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7018 - loss: 0.7448

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7018 - loss: 0.7446

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7018 - loss: 0.7444

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7018 - loss: 0.7441

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7016 - loss: 0.7439

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7014 - loss: 0.7438

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7012 - loss: 0.7437

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7010 - loss: 0.7437

126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7009 - loss: 0.7436

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7008 - loss: 0.7434

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7431

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7427

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7422

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7418

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7414

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7409

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7405

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7402

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7007 - loss: 0.7399

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7006 - loss: 0.7397

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7005 - loss: 0.7396

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7004 - loss: 0.7396

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7003 - loss: 0.7394

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7002 - loss: 0.7393

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7002 - loss: 0.7392

211/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7001 - loss: 0.7391

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7000 - loss: 0.7390

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7000 - loss: 0.7388

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6999 - loss: 0.7387

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6999 - loss: 0.7386

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6998 - loss: 0.7385

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6997 - loss: 0.7383

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6997 - loss: 0.7382

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6996 - loss: 0.7380

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6996 - loss: 0.7379

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7378

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7376

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7374

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7373

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7371

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.6995 - loss: 0.7369

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6995 - loss: 0.7367

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6995 - loss: 0.7365

300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6995 - loss: 0.7363

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6995 - loss: 0.7361

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6996 - loss: 0.7358

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6996 - loss: 0.7356

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6997 - loss: 0.7354

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7352

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7350

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7348

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6997 - loss: 0.7347

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7345

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7343

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7341

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7340

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7339

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7337

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7336

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6998 - loss: 0.7334

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6999 - loss: 0.7332

387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6999 - loss: 0.7331

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6999 - loss: 0.7329

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7327

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7326

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7324

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7323

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7321

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7000 - loss: 0.7320

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7319

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7317

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7316

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7314

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7001 - loss: 0.7313

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7311

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7002 - loss: 0.7310

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Epoch 18: val_accuracy improved from 0.71569 to 0.73418, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7002 - loss: 0.7306 - val_accuracy: 0.7342 - val_loss: 0.6616 - learning_rate: 2.0000e-04
Epoch 19/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 107ms/step - accuracy: 0.7500 - loss: 0.6538

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 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6834 - loss: 0.7137

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133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7120 - loss: 0.6897

138/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7121 - loss: 0.6902

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162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7127 - loss: 0.6911

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7128 - loss: 0.6913

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189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6918

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6919

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204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6922

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6923

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7132 - loss: 0.6925

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6928

222/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6930

227/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7131 - loss: 0.6932

231/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7130 - loss: 0.6934

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6936

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6937

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6938

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6939

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6941

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6942

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6943

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6945

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6946

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6948

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6949

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6951

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298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6955

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6957

308/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6959

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7125 - loss: 0.6960

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7124 - loss: 0.6962

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366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.6975

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381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.6978

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.6979

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7118 - loss: 0.6980

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435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7116 - loss: 0.6986

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7116 - loss: 0.6987

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6987

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6988

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7115 - loss: 0.6989

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6989

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6990

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7114 - loss: 0.6992
Epoch 19: val_accuracy did not improve from 0.73418

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7113 - loss: 0.6992 - val_accuracy: 0.7265 - val_loss: 0.6837 - learning_rate: 2.0000e-04
Epoch 20/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 53s 114ms/step - accuracy: 0.6562 - loss: 0.7046

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6496 - loss: 0.7652  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6531 - loss: 0.7556

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6555 - loss: 0.7588

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6587 - loss: 0.7646

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6625 - loss: 0.7655

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6676 - loss: 0.7607

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6711 - loss: 0.7565

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6739 - loss: 0.7525

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6759 - loss: 0.7497

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6775 - loss: 0.7474

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6789 - loss: 0.7449

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6801 - loss: 0.7424

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Epoch 20: val_accuracy improved from 0.73418 to 0.74020, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7039 - loss: 0.7124 - val_accuracy: 0.7402 - val_loss: 0.6452 - learning_rate: 2.0000e-04
Epoch 21/45
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195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6917

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6920

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6922

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6925

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6927

220/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6929

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7117 - loss: 0.6931

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6933

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6934

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6936

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6938

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6940

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6942

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6944

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6945

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6947

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6948

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6950

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6951

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6953

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6954

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6956

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7115 - loss: 0.6956

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6958

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6959

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6960

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6962

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6963

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.6964

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6965

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6966

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6967

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6967

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.6968

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6969

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6970

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.6971

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6972

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6973

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.6973

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6974

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6975

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6976

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7111 - loss: 0.6977

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6978

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6978

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6979

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6980

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7110 - loss: 0.6980

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6981

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6981

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6982

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6982

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6983

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6983

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6984

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7109 - loss: 0.6984
Epoch 21: val_accuracy did not improve from 0.74020

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7109 - loss: 0.6985 - val_accuracy: 0.7346 - val_loss: 0.6472 - learning_rate: 2.0000e-04
Epoch 22/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7500 - loss: 0.6675

  4/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.6849 - loss: 0.6972  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.6790 - loss: 0.7014

 12/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6842 - loss: 0.7015

 17/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6869 - loss: 0.7056

 22/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6879 - loss: 0.7112

 27/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6892 - loss: 0.7147

 32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6911 - loss: 0.7155

 37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.6922 - loss: 0.7168

 42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6930 - loss: 0.7172

 47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6937 - loss: 0.7174

 52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6945 - loss: 0.7174

 57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6952 - loss: 0.7171

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6960 - loss: 0.7167

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6968 - loss: 0.7161

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6973 - loss: 0.7158

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6979 - loss: 0.7154

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6985 - loss: 0.7146

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6994 - loss: 0.7135

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7002 - loss: 0.7124

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7010 - loss: 0.7114

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7018 - loss: 0.7104

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7025 - loss: 0.7094

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7032 - loss: 0.7086

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7038 - loss: 0.7078

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7045 - loss: 0.7070

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7050 - loss: 0.7064

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7056 - loss: 0.7059

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7062 - loss: 0.7053

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7068 - loss: 0.7047

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7074 - loss: 0.7041

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7034

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7085 - loss: 0.7028

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7090 - loss: 0.7023

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7095 - loss: 0.7018

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7099 - loss: 0.7014

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7103 - loss: 0.7009

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7107 - loss: 0.7005

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7110 - loss: 0.7001

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7113 - loss: 0.6998

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7115 - loss: 0.6996

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6994

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7119 - loss: 0.6992

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7121 - loss: 0.6990

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6988

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6987

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6985

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6983

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6982

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7131 - loss: 0.6981

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7133 - loss: 0.6979

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7134 - loss: 0.6978

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7136 - loss: 0.6976

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7138 - loss: 0.6975

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7139 - loss: 0.6973

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7141 - loss: 0.6972

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7141 - loss: 0.6971

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7143 - loss: 0.6970

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7143 - loss: 0.6969

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7144 - loss: 0.6968

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7145 - loss: 0.6968

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7146 - loss: 0.6967

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6967

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6966

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7148 - loss: 0.6966

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6965

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6965

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7150 - loss: 0.6964

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6964

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6964

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6964

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6964

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7153 - loss: 0.6964

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7154 - loss: 0.6964

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7155 - loss: 0.6964

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6964

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6963

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6963

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6963

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6963

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6963

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6963

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6963

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6963

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6963

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6963

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6962
Epoch 22: val_accuracy did not improve from 0.74020

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7158 - loss: 0.6962 - val_accuracy: 0.7388 - val_loss: 0.6436 - learning_rate: 2.0000e-04
Epoch 23/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.7812 - loss: 0.6125

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7504 - loss: 0.6771  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7368 - loss: 0.6731

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7335 - loss: 0.6652

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7309 - loss: 0.6642

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7280 - loss: 0.6672

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7263 - loss: 0.6693

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7248 - loss: 0.6715

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7237 - loss: 0.6732

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7229 - loss: 0.6742

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7223 - loss: 0.6753

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7218 - loss: 0.6768

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7214 - loss: 0.6777

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7212 - loss: 0.6783

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7210 - loss: 0.6789

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7206 - loss: 0.6797

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7204 - loss: 0.6804

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7201 - loss: 0.6811

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7199 - loss: 0.6817

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7197 - loss: 0.6821

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6825

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7194 - loss: 0.6830

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7194 - loss: 0.6831

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7194 - loss: 0.6832

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7195 - loss: 0.6833

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6834

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6837

133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7196 - loss: 0.6839

137/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7197 - loss: 0.6840

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6841

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6844

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6846

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6849

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6852

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6855

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6858

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6860

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6863

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6865

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6868

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6871

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6873

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7192 - loss: 0.6875

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7191 - loss: 0.6877

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7190 - loss: 0.6879

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7190 - loss: 0.6882

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7189 - loss: 0.6883

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7188 - loss: 0.6885

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7187 - loss: 0.6886

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7187 - loss: 0.6887

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7186 - loss: 0.6889

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7186 - loss: 0.6890

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7185 - loss: 0.6891

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7185 - loss: 0.6892

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7185 - loss: 0.6893

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7185 - loss: 0.6894

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6894

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6895

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6895

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6896

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7184 - loss: 0.6897

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7183 - loss: 0.6897

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7183 - loss: 0.6898

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7182 - loss: 0.6899

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7182 - loss: 0.6900

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7181 - loss: 0.6901

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7180 - loss: 0.6902

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7180 - loss: 0.6902

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7179 - loss: 0.6903

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7178 - loss: 0.6904

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7178 - loss: 0.6905

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7177 - loss: 0.6906

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7176 - loss: 0.6907

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7175 - loss: 0.6908

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7174 - loss: 0.6909

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7173 - loss: 0.6910

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7172 - loss: 0.6911

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7171 - loss: 0.6912

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7170 - loss: 0.6913

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7169 - loss: 0.6914

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6915

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6916

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6917

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6917

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7166 - loss: 0.6918

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6919

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6920

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6921

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6922

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7163 - loss: 0.6923

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Epoch 23: val_accuracy improved from 0.74020 to 0.74141, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7159 - loss: 0.6928 - val_accuracy: 0.7414 - val_loss: 0.6339 - learning_rate: 2.0000e-04
Epoch 24/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.7188 - loss: 0.6393

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137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6711

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221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6733

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6734

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7263 - loss: 0.6735

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6736

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251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6739

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6740

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311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6758

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434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7225 - loss: 0.6795

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7225 - loss: 0.6797

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7224 - loss: 0.6798

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7224 - loss: 0.6799

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7223 - loss: 0.6801

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7222 - loss: 0.6802

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7222 - loss: 0.6803

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7221 - loss: 0.6805
Epoch 24: val_accuracy did not improve from 0.74141

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7221 - loss: 0.6805 - val_accuracy: 0.7376 - val_loss: 0.6382 - learning_rate: 2.0000e-04
Epoch 25/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.8090

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 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7037 - loss: 0.6995

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7044 - loss: 0.6984

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7051 - loss: 0.6973

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7057 - loss: 0.6965

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7062 - loss: 0.6959

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.6952

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.6946

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7077 - loss: 0.6940

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7082 - loss: 0.6934

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7085 - loss: 0.6930

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7089 - loss: 0.6927

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.6925

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7095 - loss: 0.6922

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7097 - loss: 0.6920

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7100 - loss: 0.6919

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7102 - loss: 0.6917

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7105 - loss: 0.6915

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7107 - loss: 0.6913

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7110 - loss: 0.6912

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7112 - loss: 0.6910

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7114 - loss: 0.6908

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7115 - loss: 0.6908

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7116 - loss: 0.6908

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7116 - loss: 0.6908

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6908

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6909

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6910

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7117 - loss: 0.6911

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7118 - loss: 0.6911

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7118 - loss: 0.6911

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7118 - loss: 0.6912

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6913

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6913

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7120 - loss: 0.6914

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7120 - loss: 0.6913

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7121 - loss: 0.6913

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7122 - loss: 0.6913

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6913

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6913

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7124 - loss: 0.6913

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6913

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6913

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7127 - loss: 0.6912

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7128 - loss: 0.6912

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7129 - loss: 0.6911

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7130 - loss: 0.6911

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6910

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7131 - loss: 0.6910

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7132 - loss: 0.6909

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6909

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6909

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6908

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7135 - loss: 0.6908

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6907

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6906

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7137 - loss: 0.6905

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7138 - loss: 0.6905

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6904

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6903

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7140 - loss: 0.6902

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7141 - loss: 0.6901

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7141 - loss: 0.6900

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6899

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6898

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7143 - loss: 0.6898

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7143 - loss: 0.6898

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7144 - loss: 0.6898

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7145 - loss: 0.6897

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7145 - loss: 0.6897

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7146 - loss: 0.6897

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6897

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6897

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7147 - loss: 0.6898

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6898

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7148 - loss: 0.6899

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7149 - loss: 0.6899
Epoch 25: val_accuracy did not improve from 0.74141

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7149 - loss: 0.6899 - val_accuracy: 0.7376 - val_loss: 0.6535 - learning_rate: 2.0000e-04
Epoch 26/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.7500 - loss: 0.6739

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7461 - loss: 0.6826  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7484 - loss: 0.6843

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7427 - loss: 0.6857

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7388 - loss: 0.6892

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7360 - loss: 0.6903

 31/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7351 - loss: 0.6894

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7338 - loss: 0.6892

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7327 - loss: 0.6883

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7318 - loss: 0.6875

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7313 - loss: 0.6865

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7309 - loss: 0.6855

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7303 - loss: 0.6848

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7296 - loss: 0.6845

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7291 - loss: 0.6843

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7289 - loss: 0.6839

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7286 - loss: 0.6838

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7284 - loss: 0.6835

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7282 - loss: 0.6830

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7282 - loss: 0.6825

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7281 - loss: 0.6822

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7280 - loss: 0.6819

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7278 - loss: 0.6817

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7277 - loss: 0.6815

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7276 - loss: 0.6814

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7275 - loss: 0.6813

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6812

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7272 - loss: 0.6811

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7271 - loss: 0.6811

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7269 - loss: 0.6811

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7267 - loss: 0.6812

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7265 - loss: 0.6814

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7262 - loss: 0.6816

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7260 - loss: 0.6819

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6822

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6825

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6828

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6831

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6833

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6835

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6837

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Epoch 26: val_accuracy improved from 0.74141 to 0.74844, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7212 - loss: 0.6871 - val_accuracy: 0.7484 - val_loss: 0.6241 - learning_rate: 2.0000e-04
Epoch 27/45
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332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7127 - loss: 0.6963

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342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7129 - loss: 0.6961

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6960

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6959

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7131 - loss: 0.6957

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7132 - loss: 0.6956

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7132 - loss: 0.6955

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6954

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6953

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6952

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7135 - loss: 0.6951

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7135 - loss: 0.6950

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7136 - loss: 0.6950

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7136 - loss: 0.6949

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7137 - loss: 0.6948

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7137 - loss: 0.6948

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7137 - loss: 0.6947

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7138 - loss: 0.6946

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7138 - loss: 0.6946

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7139 - loss: 0.6945

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7139 - loss: 0.6944

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7140 - loss: 0.6944

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7140 - loss: 0.6943

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6942

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6942

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7141 - loss: 0.6941

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7142 - loss: 0.6940
Epoch 27: val_accuracy did not improve from 0.74844

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7143 - loss: 0.6939 - val_accuracy: 0.7450 - val_loss: 0.6338 - learning_rate: 2.0000e-04
Epoch 28/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5938 - loss: 0.7196

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6997 - loss: 0.6371  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7130 - loss: 0.6291

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7204 - loss: 0.6282

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7226 - loss: 0.6314

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7250 - loss: 0.6342

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7261 - loss: 0.6394

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7266 - loss: 0.6439

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7274 - loss: 0.6461

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6477

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6500

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6516

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6527

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7294 - loss: 0.6538

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7295 - loss: 0.6548

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7293 - loss: 0.6560

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7291 - loss: 0.6572

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7288 - loss: 0.6584

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7286 - loss: 0.6595

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6606

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6618

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6629

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6637

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6645

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6650

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6654

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7279 - loss: 0.6657

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7279 - loss: 0.6661

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7278 - loss: 0.6666

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7277 - loss: 0.6670

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7277 - loss: 0.6673

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6676

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7276 - loss: 0.6678

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7275 - loss: 0.6682

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7274 - loss: 0.6686

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7273 - loss: 0.6689

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7272 - loss: 0.6692

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7271 - loss: 0.6694

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7271 - loss: 0.6695

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7270 - loss: 0.6697

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7270 - loss: 0.6698

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7269 - loss: 0.6699

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7269 - loss: 0.6701

215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6702

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6704

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6706

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6708

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6710

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6712

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7263 - loss: 0.6714

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6715

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6717

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6718

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6719

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6720

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6722

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7258 - loss: 0.6723

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7257 - loss: 0.6724

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7257 - loss: 0.6726

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7256 - loss: 0.6727

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7255 - loss: 0.6729

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7254 - loss: 0.6731

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7253 - loss: 0.6732

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7252 - loss: 0.6734

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7251 - loss: 0.6735

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7250 - loss: 0.6736

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7250 - loss: 0.6738

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7249 - loss: 0.6739

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6740

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7248 - loss: 0.6741

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7247 - loss: 0.6742

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6744

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7246 - loss: 0.6745

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6746

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7245 - loss: 0.6747

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7244 - loss: 0.6748

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7243 - loss: 0.6749

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7243 - loss: 0.6749

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7242 - loss: 0.6750

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7242 - loss: 0.6751

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6752

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6752

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6753

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6754

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6755

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6755

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6756

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6756

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6757

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6757

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6758

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6758

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7234 - loss: 0.6759

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7234 - loss: 0.6759

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7233 - loss: 0.6760

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7232 - loss: 0.6761
Epoch 28: val_accuracy did not improve from 0.74844

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7232 - loss: 0.6761 - val_accuracy: 0.7470 - val_loss: 0.6271 - learning_rate: 2.0000e-04
Epoch 29/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.6250 - loss: 0.7499

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Epoch 29: val_accuracy improved from 0.74844 to 0.75166, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7234 - loss: 0.6711 - val_accuracy: 0.7517 - val_loss: 0.6202 - learning_rate: 2.0000e-04
Epoch 30/45
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 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7251 - loss: 0.6651

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7253 - loss: 0.6652

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7254 - loss: 0.6652

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7255 - loss: 0.6653

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7256 - loss: 0.6655

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7256 - loss: 0.6657

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7256 - loss: 0.6658

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7256 - loss: 0.6659

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6659

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6658

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6659

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6660

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6661

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6664

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6666

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6667

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7256 - loss: 0.6669

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7256 - loss: 0.6671

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6672

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6673

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6674

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6676

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6677

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7252 - loss: 0.6679

215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7251 - loss: 0.6681

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7250 - loss: 0.6683

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7249 - loss: 0.6685

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7247 - loss: 0.6687

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7246 - loss: 0.6689

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7245 - loss: 0.6691

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7244 - loss: 0.6693

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7243 - loss: 0.6694

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7243 - loss: 0.6695

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7242 - loss: 0.6696

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6697

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7241 - loss: 0.6698

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7240 - loss: 0.6698

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7240 - loss: 0.6699

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6699

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7239 - loss: 0.6700

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6700

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7238 - loss: 0.6701

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7238 - loss: 0.6702

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6702

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6703

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7237 - loss: 0.6704

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6704

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6705

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6706

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6707

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6707

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6707

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6708

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6708

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6708

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7235 - loss: 0.6708

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6708

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6708

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7236 - loss: 0.6709

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6708
Epoch 30: val_accuracy did not improve from 0.75166

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7237 - loss: 0.6708 - val_accuracy: 0.7402 - val_loss: 0.6328 - learning_rate: 2.0000e-04
Epoch 31/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:09 147ms/step - accuracy: 0.8438 - loss: 0.5112

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.8118 - loss: 0.5421   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.7935 - loss: 0.5802

 11/473 ━━━━━━━━━━━━━━━━━━━━ 7s 17ms/step - accuracy: 0.7715 - loss: 0.6157

 16/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.7552 - loss: 0.6465

 21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7457 - loss: 0.6638

 26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7390 - loss: 0.6742

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7346 - loss: 0.6803

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7323 - loss: 0.6833

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7301 - loss: 0.6863

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7283 - loss: 0.6896

 51/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7269 - loss: 0.6925

 56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7256 - loss: 0.6951

 61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7244 - loss: 0.6968

 66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7236 - loss: 0.6977

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7230 - loss: 0.6983

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7225 - loss: 0.6985

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7222 - loss: 0.6986

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7219 - loss: 0.6985

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7216 - loss: 0.6984

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7214 - loss: 0.6984

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7212 - loss: 0.6984

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7210 - loss: 0.6983

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7209 - loss: 0.6981

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7209 - loss: 0.6980

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7208 - loss: 0.6978

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7207 - loss: 0.6977

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7206 - loss: 0.6976

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7205 - loss: 0.6974

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7204 - loss: 0.6973

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7202 - loss: 0.6973

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7200 - loss: 0.6973

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7199 - loss: 0.6974

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7197 - loss: 0.6974

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6973

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6973

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6972

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6971

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6970

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7193 - loss: 0.6968

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6966

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6963

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6961

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7195 - loss: 0.6958

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6956

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7197 - loss: 0.6953

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7198 - loss: 0.6951

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7198 - loss: 0.6948

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6946

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6944

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7199 - loss: 0.6941

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7200 - loss: 0.6939

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7200 - loss: 0.6937

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7201 - loss: 0.6935

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7202 - loss: 0.6932

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7202 - loss: 0.6930

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7203 - loss: 0.6927

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7204 - loss: 0.6925

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7204 - loss: 0.6922

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6920

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6919

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6918

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7205 - loss: 0.6916

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6915

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6914

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6912

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6911

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6910

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.6909

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6908

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6907

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6906

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6905

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6903

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6902

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6901

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6900

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6899

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7208 - loss: 0.6898

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6896

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6895

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7207 - loss: 0.6894

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6893

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6891

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6890

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6889

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6887

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6886

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6885

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6884

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6883

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6881

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7208 - loss: 0.6880

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6879

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6878

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6877

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6876

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7209 - loss: 0.6875
Epoch 31: val_accuracy did not improve from 0.75166

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7209 - loss: 0.6874 - val_accuracy: 0.7402 - val_loss: 0.6422 - learning_rate: 2.0000e-04
Epoch 32/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 112ms/step - accuracy: 0.8125 - loss: 0.6429

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8084 - loss: 0.5991  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7823 - loss: 0.6129

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7710 - loss: 0.6174

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7673 - loss: 0.6163

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7635 - loss: 0.6186

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7610 - loss: 0.6199

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7591 - loss: 0.6217

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7580 - loss: 0.6223

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7575 - loss: 0.6225

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7572 - loss: 0.6229

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7569 - loss: 0.6234

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7564 - loss: 0.6239

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7559 - loss: 0.6244

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7555 - loss: 0.6247

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7551 - loss: 0.6251

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7546 - loss: 0.6256

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7540 - loss: 0.6266

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7533 - loss: 0.6276

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7525 - loss: 0.6287

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7516 - loss: 0.6299

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7508 - loss: 0.6312

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7501 - loss: 0.6323

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7495 - loss: 0.6333

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7489 - loss: 0.6343

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7484 - loss: 0.6352

129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7479 - loss: 0.6360

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7474 - loss: 0.6367

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7470 - loss: 0.6373

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7467 - loss: 0.6378

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7463 - loss: 0.6384

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7458 - loss: 0.6390

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7454 - loss: 0.6396

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7450 - loss: 0.6401

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7447 - loss: 0.6406

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7443 - loss: 0.6410

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7440 - loss: 0.6414

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7437 - loss: 0.6418

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7435 - loss: 0.6420

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7432 - loss: 0.6423

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7430 - loss: 0.6426

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7428 - loss: 0.6430

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7425 - loss: 0.6433

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7423 - loss: 0.6436

217/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7421 - loss: 0.6439

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7419 - loss: 0.6443

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7417 - loss: 0.6445

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7415 - loss: 0.6448

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7414 - loss: 0.6451

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7412 - loss: 0.6454

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7410 - loss: 0.6458

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7408 - loss: 0.6461

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7407 - loss: 0.6464

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7405 - loss: 0.6468

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6471

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7400 - loss: 0.6475

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7398 - loss: 0.6478

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7396 - loss: 0.6482

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7394 - loss: 0.6485

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7392 - loss: 0.6489

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7390 - loss: 0.6493

301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7389 - loss: 0.6496

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7387 - loss: 0.6500

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7385 - loss: 0.6503

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7383 - loss: 0.6507

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7381 - loss: 0.6510

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6514

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6517

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6520

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6523

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6525

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6528

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7371 - loss: 0.6530

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7369 - loss: 0.6533

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7368 - loss: 0.6535

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7367 - loss: 0.6537

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7365 - loss: 0.6539

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7364 - loss: 0.6542

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7363 - loss: 0.6544

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7361 - loss: 0.6547

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7360 - loss: 0.6549

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7359 - loss: 0.6551

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7357 - loss: 0.6554

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7356 - loss: 0.6556

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7355 - loss: 0.6558

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7354 - loss: 0.6560

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7353 - loss: 0.6562

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7352 - loss: 0.6564

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7350 - loss: 0.6566

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7349 - loss: 0.6567

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7348 - loss: 0.6569

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7347 - loss: 0.6571

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7346 - loss: 0.6573

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7345 - loss: 0.6574

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7345 - loss: 0.6576
Epoch 32: val_accuracy did not improve from 0.75166

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7343 - loss: 0.6578 - val_accuracy: 0.7402 - val_loss: 0.6478 - learning_rate: 2.0000e-04
Epoch 33/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.6875 - loss: 0.6361

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6595 - loss: 0.7662  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6544 - loss: 0.7604

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6579 - loss: 0.7529

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6648 - loss: 0.7410

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6702 - loss: 0.7317

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6740 - loss: 0.7243

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6776 - loss: 0.7193

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6817 - loss: 0.7140

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6850 - loss: 0.7098

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6879 - loss: 0.7065

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6903 - loss: 0.7037

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6921 - loss: 0.7011

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6936 - loss: 0.6987

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6950 - loss: 0.6963

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6960 - loss: 0.6949

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6970 - loss: 0.6934

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6980 - loss: 0.6918

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6990 - loss: 0.6904

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6999 - loss: 0.6892

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7007 - loss: 0.6881

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7015 - loss: 0.6872

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7022 - loss: 0.6864

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7028 - loss: 0.6857

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7034 - loss: 0.6851

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7040 - loss: 0.6844

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7046 - loss: 0.6838

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7051 - loss: 0.6833

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7055 - loss: 0.6828

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7059 - loss: 0.6824

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7062 - loss: 0.6822

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7066 - loss: 0.6819

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7070 - loss: 0.6817

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7073 - loss: 0.6815

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7076 - loss: 0.6813

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.6811

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.6810

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7084 - loss: 0.6809

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7086 - loss: 0.6808

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7088 - loss: 0.6807

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7090 - loss: 0.6807

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.6806

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.6805

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7096 - loss: 0.6804

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7098 - loss: 0.6803

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7100 - loss: 0.6801

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7102 - loss: 0.6800

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7104 - loss: 0.6799

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7105 - loss: 0.6799

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7107 - loss: 0.6798

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7109 - loss: 0.6797

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7110 - loss: 0.6795

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7112 - loss: 0.6794

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7114 - loss: 0.6793

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7116 - loss: 0.6792

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7117 - loss: 0.6790

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7119 - loss: 0.6789

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7121 - loss: 0.6788

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7123 - loss: 0.6787

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7125 - loss: 0.6785

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7126 - loss: 0.6784

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7128 - loss: 0.6783

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7130 - loss: 0.6782

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7131 - loss: 0.6781

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7133 - loss: 0.6780

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7134 - loss: 0.6779

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7136 - loss: 0.6778

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7137 - loss: 0.6777

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7139 - loss: 0.6776

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7140 - loss: 0.6775

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7142 - loss: 0.6774

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7143 - loss: 0.6773

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7144 - loss: 0.6772

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7145 - loss: 0.6771

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7147 - loss: 0.6770

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7148 - loss: 0.6769

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7149 - loss: 0.6768

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7151 - loss: 0.6766

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7152 - loss: 0.6766

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7153 - loss: 0.6765

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7154 - loss: 0.6764

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7155 - loss: 0.6763

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7156 - loss: 0.6762

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7157 - loss: 0.6761

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7158 - loss: 0.6760

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7159 - loss: 0.6759

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7160 - loss: 0.6758

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7160 - loss: 0.6758

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7161 - loss: 0.6757

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7162 - loss: 0.6757

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7163 - loss: 0.6756

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7164 - loss: 0.6754

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7165 - loss: 0.6753

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7166 - loss: 0.6752

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7167 - loss: 0.6751

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7168 - loss: 0.6750

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7169 - loss: 0.6749

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7171 - loss: 0.6748
Epoch 33: val_accuracy did not improve from 0.75166

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7172 - loss: 0.6747 - val_accuracy: 0.7468 - val_loss: 0.6348 - learning_rate: 2.0000e-04
Epoch 34/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.7812 - loss: 0.4825

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7055 - loss: 0.6792  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7106 - loss: 0.6819

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7215 - loss: 0.6709

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7303 - loss: 0.6605

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7339 - loss: 0.6563

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7350 - loss: 0.6544

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7358 - loss: 0.6540

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7360 - loss: 0.6533

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7355 - loss: 0.6536

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7353 - loss: 0.6536

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7352 - loss: 0.6540

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7349 - loss: 0.6547

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6554

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6553

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6551

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6549

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6545

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7344 - loss: 0.6540

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6533

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7347 - loss: 0.6526

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6521

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6515

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7348 - loss: 0.6511

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7346 - loss: 0.6509

126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7344 - loss: 0.6508

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7342 - loss: 0.6508

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7340 - loss: 0.6507

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7339 - loss: 0.6507

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7338 - loss: 0.6507

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7337 - loss: 0.6507

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7336 - loss: 0.6507

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7335 - loss: 0.6506

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7334 - loss: 0.6505

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7333 - loss: 0.6505

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7332 - loss: 0.6506

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7332 - loss: 0.6506

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7330 - loss: 0.6507

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7330 - loss: 0.6507

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7329 - loss: 0.6508

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7328 - loss: 0.6508

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7326 - loss: 0.6509

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7325 - loss: 0.6510

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7324 - loss: 0.6511

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7323 - loss: 0.6513

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7321 - loss: 0.6514

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7320 - loss: 0.6515

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7319 - loss: 0.6516

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7318 - loss: 0.6516

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7317 - loss: 0.6517

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6517

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6518

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6518

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6519

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6520

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6520

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6521

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6522

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6523

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6524

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7309 - loss: 0.6525

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7309 - loss: 0.6526

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7308 - loss: 0.6527

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6528

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6529

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7306 - loss: 0.6531

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7306 - loss: 0.6532

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7305 - loss: 0.6533

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7305 - loss: 0.6534

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7304 - loss: 0.6536

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7303 - loss: 0.6537

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7303 - loss: 0.6538

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7302 - loss: 0.6540

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7302 - loss: 0.6541

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6543

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6544

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6545

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6546

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6547

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7298 - loss: 0.6549

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6550

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6551

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6552

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7297 - loss: 0.6553

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7296 - loss: 0.6554

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6556

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6557

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6558

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6559

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7293 - loss: 0.6561

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6562

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6563

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6565

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6566

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7290 - loss: 0.6568

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6569

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6570
Epoch 34: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 34: val_accuracy did not improve from 0.75166

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7288 - loss: 0.6572 - val_accuracy: 0.7364 - val_loss: 0.6530 - learning_rate: 2.0000e-04
Epoch 35/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.7188 - loss: 0.6853

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6864 - loss: 0.7151  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7080 - loss: 0.6850

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7169 - loss: 0.6678

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7186 - loss: 0.6629

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7206 - loss: 0.6591

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7223 - loss: 0.6571

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7236 - loss: 0.6549

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7247 - loss: 0.6533

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7254 - loss: 0.6527

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7262 - loss: 0.6521

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7267 - loss: 0.6520

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7275 - loss: 0.6521

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6527

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6536

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6547

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6554

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7281 - loss: 0.6559

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6562

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6563

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Epoch 35: val_accuracy improved from 0.75166 to 0.75829, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7280 - loss: 0.6650 - val_accuracy: 0.7583 - val_loss: 0.6128 - learning_rate: 4.0000e-05
Epoch 36/45
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224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6477

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6478

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6478

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7293 - loss: 0.6478

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7294 - loss: 0.6479

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7295 - loss: 0.6480

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7295 - loss: 0.6481

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7296 - loss: 0.6482

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7296 - loss: 0.6484

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7297 - loss: 0.6484

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7297 - loss: 0.6485

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7298 - loss: 0.6486

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7298 - loss: 0.6488

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7298 - loss: 0.6489

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7299 - loss: 0.6490

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7299 - loss: 0.6491

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6492

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6493

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7299 - loss: 0.6494

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6496

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6497

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6498

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6499

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6500

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6501

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6502

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7300 - loss: 0.6503

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6504

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6505

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7301 - loss: 0.6505

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7301 - loss: 0.6506

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7301 - loss: 0.6507

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7302 - loss: 0.6507

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7302 - loss: 0.6507

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7302 - loss: 0.6508

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6508

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6509

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7303 - loss: 0.6510

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6510

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6511

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6511

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7304 - loss: 0.6512

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6513

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7305 - loss: 0.6514
Epoch 36: val_accuracy did not improve from 0.75829

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7305 - loss: 0.6514 - val_accuracy: 0.7553 - val_loss: 0.6146 - learning_rate: 4.0000e-05
Epoch 37/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 56s 120ms/step - accuracy: 0.7500 - loss: 0.5479

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6908 - loss: 0.6815  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6932 - loss: 0.6756

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6996 - loss: 0.6636

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7045 - loss: 0.6562

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7089 - loss: 0.6501

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7124 - loss: 0.6490

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7138 - loss: 0.6498

 40/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7146 - loss: 0.6511

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7153 - loss: 0.6524

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7163 - loss: 0.6528

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7169 - loss: 0.6530

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7175 - loss: 0.6531

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6531

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7185 - loss: 0.6532

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7190 - loss: 0.6532

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7196 - loss: 0.6531

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7202 - loss: 0.6531

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7207 - loss: 0.6533

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7211 - loss: 0.6536

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7215 - loss: 0.6540

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7219 - loss: 0.6542

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7222 - loss: 0.6543

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7226 - loss: 0.6543

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7229 - loss: 0.6543

125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7232 - loss: 0.6545

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7235 - loss: 0.6546

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7238 - loss: 0.6547

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7240 - loss: 0.6548

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7243 - loss: 0.6549

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7245 - loss: 0.6549

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7246 - loss: 0.6550

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7247 - loss: 0.6551

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6552

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6554

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6556

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7251 - loss: 0.6557

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6559

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7254 - loss: 0.6559

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7255 - loss: 0.6560

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7256 - loss: 0.6562

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7257 - loss: 0.6563

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7258 - loss: 0.6564

215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7259 - loss: 0.6566

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7260 - loss: 0.6567

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7261 - loss: 0.6568

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7261 - loss: 0.6570

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7262 - loss: 0.6571

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6572

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6572

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7264 - loss: 0.6572

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7265 - loss: 0.6572

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7266 - loss: 0.6572

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6573

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7267 - loss: 0.6573

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6574

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6574

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7268 - loss: 0.6575

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7269 - loss: 0.6575

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7269 - loss: 0.6575

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7270 - loss: 0.6575

301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7270 - loss: 0.6575

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6575

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6574

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6574

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6574

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6573

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6573

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6573

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6572

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6572

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7278 - loss: 0.6571

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7278 - loss: 0.6571

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7279 - loss: 0.6570

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7280 - loss: 0.6570

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6570

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6569

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6569

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6569

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6569

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7284 - loss: 0.6569

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7285 - loss: 0.6569

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7285 - loss: 0.6569

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7286 - loss: 0.6568

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7287 - loss: 0.6568

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7288 - loss: 0.6568

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7288 - loss: 0.6568

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7289 - loss: 0.6568

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7290 - loss: 0.6567

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6567

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7291 - loss: 0.6567

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7292 - loss: 0.6566

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7293 - loss: 0.6566

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6565

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7294 - loss: 0.6565

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7295 - loss: 0.6564
Epoch 37: val_accuracy did not improve from 0.75829

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7296 - loss: 0.6564 - val_accuracy: 0.7549 - val_loss: 0.6183 - learning_rate: 4.0000e-05
Epoch 38/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 50s 106ms/step - accuracy: 0.6875 - loss: 0.6545

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7509 - loss: 0.6073  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7607 - loss: 0.6014

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7620 - loss: 0.5998

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7638 - loss: 0.5966

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7639 - loss: 0.5943

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7640 - loss: 0.5927

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7630 - loss: 0.5938

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7622 - loss: 0.5950

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7614 - loss: 0.5963

 50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7608 - loss: 0.5972

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7598 - loss: 0.5987

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7591 - loss: 0.5998

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7584 - loss: 0.6010

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7575 - loss: 0.6026

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7565 - loss: 0.6043

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7556 - loss: 0.6058

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7547 - loss: 0.6071

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7540 - loss: 0.6081

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7532 - loss: 0.6091

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7523 - loss: 0.6102

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7514 - loss: 0.6113

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7506 - loss: 0.6124

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7498 - loss: 0.6136

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7491 - loss: 0.6148

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7484 - loss: 0.6159

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7478 - loss: 0.6170

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7472 - loss: 0.6180

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7467 - loss: 0.6189

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7461 - loss: 0.6198

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7456 - loss: 0.6209

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7451 - loss: 0.6219

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7446 - loss: 0.6228

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7442 - loss: 0.6238

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7438 - loss: 0.6245

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7434 - loss: 0.6255

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7430 - loss: 0.6264

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7427 - loss: 0.6271

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7423 - loss: 0.6279

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7420 - loss: 0.6286

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7418 - loss: 0.6293

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7415 - loss: 0.6299

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7413 - loss: 0.6304

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7411 - loss: 0.6309

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7408 - loss: 0.6315

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7406 - loss: 0.6321

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6327

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7401 - loss: 0.6332

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7399 - loss: 0.6337

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7397 - loss: 0.6342

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7395 - loss: 0.6346

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7394 - loss: 0.6350

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7392 - loss: 0.6353

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7391 - loss: 0.6357

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7390 - loss: 0.6360

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7388 - loss: 0.6363

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7387 - loss: 0.6366

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7386 - loss: 0.6370

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7385 - loss: 0.6372

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7383 - loss: 0.6376

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7382 - loss: 0.6380

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7381 - loss: 0.6384

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6386

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7380 - loss: 0.6389

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7379 - loss: 0.6392

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6395

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6397

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6400

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6402

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6404

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6406

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6408

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6409

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6411

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6412

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6414

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6415

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7372 - loss: 0.6417

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7371 - loss: 0.6419

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6420

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6422

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6423

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6424

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6425

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6427

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6428

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6429

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6430

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6431

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6432

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6432

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6433

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6434

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6435

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6436

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6437
Epoch 38: val_accuracy did not improve from 0.75829

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7368 - loss: 0.6438 - val_accuracy: 0.7563 - val_loss: 0.6083 - learning_rate: 4.0000e-05
Epoch 39/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 52s 111ms/step - accuracy: 0.7812 - loss: 0.5779

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8012 - loss: 0.5409  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7870 - loss: 0.5563

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7816 - loss: 0.5612

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7780 - loss: 0.5658

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7748 - loss: 0.5722

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7726 - loss: 0.5755

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7708 - loss: 0.5775

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7690 - loss: 0.5809

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7669 - loss: 0.5847

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7653 - loss: 0.5876

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7635 - loss: 0.5909

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7617 - loss: 0.5943

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7601 - loss: 0.5975

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7587 - loss: 0.5999

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7576 - loss: 0.6018

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7566 - loss: 0.6033

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7555 - loss: 0.6049

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7543 - loss: 0.6065

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7533 - loss: 0.6081

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.6095

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7518 - loss: 0.6105

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7513 - loss: 0.6114

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7508 - loss: 0.6121

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7504 - loss: 0.6128

125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7499 - loss: 0.6135

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7496 - loss: 0.6140

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7492 - loss: 0.6144

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7489 - loss: 0.6148

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7486 - loss: 0.6154

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7482 - loss: 0.6160

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7479 - loss: 0.6166

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7476 - loss: 0.6172

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7473 - loss: 0.6177

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7471 - loss: 0.6181

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7468 - loss: 0.6186

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7466 - loss: 0.6190

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7463 - loss: 0.6195

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7461 - loss: 0.6199

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7459 - loss: 0.6202

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7458 - loss: 0.6206

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7456 - loss: 0.6209

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7455 - loss: 0.6212

215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7453 - loss: 0.6215

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7452 - loss: 0.6218

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7450 - loss: 0.6221

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7449 - loss: 0.6224

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7448 - loss: 0.6227

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7447 - loss: 0.6230

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7447 - loss: 0.6232

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7446 - loss: 0.6234

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7446 - loss: 0.6236

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7445 - loss: 0.6237

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7445 - loss: 0.6239

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7444 - loss: 0.6241

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7443 - loss: 0.6243

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7443 - loss: 0.6245

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7442 - loss: 0.6247

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7441 - loss: 0.6249

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7440 - loss: 0.6252

300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7439 - loss: 0.6254

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7438 - loss: 0.6256

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7437 - loss: 0.6258

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7436 - loss: 0.6260

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7435 - loss: 0.6262

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7434 - loss: 0.6265

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7433 - loss: 0.6267

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7432 - loss: 0.6269

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7431 - loss: 0.6271

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7430 - loss: 0.6273

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7429 - loss: 0.6275

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7428 - loss: 0.6276

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7428 - loss: 0.6278

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7427 - loss: 0.6280

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7426 - loss: 0.6281

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7425 - loss: 0.6283

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7425 - loss: 0.6284

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7424 - loss: 0.6285

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7423 - loss: 0.6287

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7422 - loss: 0.6288

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7421 - loss: 0.6290

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7421 - loss: 0.6292

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7420 - loss: 0.6293

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7419 - loss: 0.6294

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7419 - loss: 0.6296

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7418 - loss: 0.6297

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7417 - loss: 0.6299

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7417 - loss: 0.6300

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7416 - loss: 0.6302

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7416 - loss: 0.6303

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7415 - loss: 0.6305

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7414 - loss: 0.6307

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7414 - loss: 0.6308

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7413 - loss: 0.6310

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7412 - loss: 0.6312
Epoch 39: val_accuracy did not improve from 0.75829

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7412 - loss: 0.6313 - val_accuracy: 0.7575 - val_loss: 0.6137 - learning_rate: 4.0000e-05
Epoch 40/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:01 130ms/step - accuracy: 0.5938 - loss: 0.9132

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6492 - loss: 0.7829   

  9/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6754 - loss: 0.7277

 13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.6891 - loss: 0.7007

 16/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.6963 - loss: 0.6902

 21/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7061 - loss: 0.6784

 26/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7122 - loss: 0.6708

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7165 - loss: 0.6642

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7191 - loss: 0.6595

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7215 - loss: 0.6553

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7239 - loss: 0.6513

 50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7255 - loss: 0.6487

 55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7269 - loss: 0.6471

 61/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7285 - loss: 0.6453

 66/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7296 - loss: 0.6436

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7306 - loss: 0.6420

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7316 - loss: 0.6407

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7324 - loss: 0.6395

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7330 - loss: 0.6385

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7336 - loss: 0.6376

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7339 - loss: 0.6370

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7343 - loss: 0.6367

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7346 - loss: 0.6362

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7350 - loss: 0.6356

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7353 - loss: 0.6351

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7356 - loss: 0.6346

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7358 - loss: 0.6343

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7360 - loss: 0.6341

134/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7362 - loss: 0.6338

139/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7364 - loss: 0.6337

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6335

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6334

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6333

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7369 - loss: 0.6332

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6332

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6332

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6333

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6335

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7370 - loss: 0.6336

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6337

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6337

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7371 - loss: 0.6338

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6339

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6339

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7372 - loss: 0.6340

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7373 - loss: 0.6341

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7373 - loss: 0.6341

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7374 - loss: 0.6342

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7374 - loss: 0.6342

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7375 - loss: 0.6343

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6344

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6345

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7376 - loss: 0.6346

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6347

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6348

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6349

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7377 - loss: 0.6350

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6351

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6352

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6353

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6355

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6356

305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7378 - loss: 0.6358

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6359

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6360

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6361

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6363

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7378 - loss: 0.6364

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6365

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7377 - loss: 0.6367

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6368

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6369

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7376 - loss: 0.6371

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6372

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7375 - loss: 0.6373

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6374

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7374 - loss: 0.6375

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6376

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6377

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7373 - loss: 0.6378

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6379

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6380

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7372 - loss: 0.6380

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6381

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6382

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7371 - loss: 0.6383

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6383

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6384

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7370 - loss: 0.6385

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6386

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6387

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6387

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7369 - loss: 0.6388

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6389

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6390

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7368 - loss: 0.6391

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7367 - loss: 0.6392
Epoch 40: val_accuracy did not improve from 0.75829

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7367 - loss: 0.6393 - val_accuracy: 0.7551 - val_loss: 0.6151 - learning_rate: 4.0000e-05
Epoch 41/45
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.5863

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.7682 - loss: 0.6187  

  9/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7584 - loss: 0.6302

 13/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7559 - loss: 0.6265

 18/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7536 - loss: 0.6267

 23/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7520 - loss: 0.6311

 28/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7503 - loss: 0.6375

 33/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7492 - loss: 0.6416

 38/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7484 - loss: 0.6446

 43/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7473 - loss: 0.6473

 48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7464 - loss: 0.6488

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7454 - loss: 0.6501

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7444 - loss: 0.6517

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7433 - loss: 0.6532

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7424 - loss: 0.6546

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7416 - loss: 0.6557

 78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7411 - loss: 0.6562

 83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7408 - loss: 0.6567

 88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7405 - loss: 0.6571

 93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7402 - loss: 0.6573

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7400 - loss: 0.6574

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7397 - loss: 0.6573

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7396 - loss: 0.6572

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7395 - loss: 0.6570

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7395 - loss: 0.6567

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7396 - loss: 0.6563

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7397 - loss: 0.6560

132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7398 - loss: 0.6558

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7398 - loss: 0.6556

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6555

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6552

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7397 - loss: 0.6551

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7398 - loss: 0.6548

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7398 - loss: 0.6546

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7399 - loss: 0.6544

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7399 - loss: 0.6542

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7400 - loss: 0.6540

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7401 - loss: 0.6538

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7401 - loss: 0.6536

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6535

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6534

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7402 - loss: 0.6533

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6531

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6530

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6529

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7403 - loss: 0.6529

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6528

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7403 - loss: 0.6527

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Epoch 41: val_accuracy improved from 0.75829 to 0.75909, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7391 - loss: 0.6507 - val_accuracy: 0.7591 - val_loss: 0.6059 - learning_rate: 4.0000e-05
Epoch 42/45
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Epoch 42: val_accuracy did not improve from 0.75909

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7372 - loss: 0.6434 - val_accuracy: 0.7585 - val_loss: 0.6082 - learning_rate: 4.0000e-05
Epoch 43/45
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Epoch 43: val_accuracy improved from 0.75909 to 0.76150, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7266 - loss: 0.6557 - val_accuracy: 0.7615 - val_loss: 0.6053 - learning_rate: 4.0000e-05
Epoch 44/45
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Epoch 44: val_accuracy improved from 0.76150 to 0.76211, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_2_20240411-000906.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7386 - loss: 0.6314 - val_accuracy: 0.7621 - val_loss: 0.6069 - learning_rate: 4.0000e-05
Epoch 45/45
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Epoch 45: val_accuracy did not improve from 0.76211

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7328 - loss: 0.6400 - val_accuracy: 0.7569 - val_loss: 0.6118 - learning_rate: 4.0000e-05
Restoring model weights from the end of the best epoch: 43.

Plotting the Training and Validation Accuracies¶

In [29]:
plt.plot(history_2.history["accuracy"])
plt.plot(history_2.history["val_accuracy"])
plt.title("CNN Model 2 accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the Model on the Test Set¶

In [30]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = model_2.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8125 - loss: 0.4481

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7875 - loss: 0.5252 
Loss: 0.5420138835906982, Accuracy: 0.7890625

Plotting Confusion Matrix¶

In [31]:
pred_probabilities = model_2.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("CNN Model 2 Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 394ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step  
              precision    recall  f1-score   support

       happy       0.81      0.81      0.81        32
     neutral       0.66      0.78      0.71        32
         sad       0.75      0.66      0.70        32
    surprise       0.97      0.91      0.94        32

    accuracy                           0.79       128
   macro avg       0.80      0.79      0.79       128
weighted avg       0.80      0.79      0.79       128

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Observations and Insights:

  • The model's total parameters are 397,860, which includes 396,772 trainable parameters.
  • Obtained a 78.90% accuracy on the test set, surpassing the first model's test accuracy.
  • Precision and recall improvements are noted in 'neutral' predictions, while 'happy' and 'surprise' maintain strong performance.
  • The neutral and 'sad' classes continue to be challenging for the model, with lower comparative metrics.

Think About It:¶

  • Did the models have a satisfactory performance? If not, then what are the possible reasons?
  • Answer: The models had moderate performance, up to 79%, with room for improvement and challenges in distinguishing between certain emotions, particularly 'sad' and 'neutral'.
  • Which Color mode showed better overall performance? What are the possible reasons? Do you think having 'rgb' color mode is needed because the images are already black and white?
  • Answer: Grayscale mode yielded sufficient performance given the nature of the data, and using 'rgb' color mode for grayscale images did not provide additional benefits as the color information does not contribute to emotion recognition in this context.

Transfer Learning Architectures¶

In this section, we will create several Transfer Learning architectures. For the pre-trained models, we will select three popular architectures namely, VGG16, ResNet v2, and Efficient Net. The difference between these architectures and the previous architectures is that these will require 3 input channels while the earlier ones worked on 'grayscale' images. Therefore, we need to create new DataLoaders.

Creating our Data Loaders for Transfer Learning Architectures¶

In this section, we are creating data loaders that we will use as inputs to our Neural Network. We will have to go with color_mode = 'rgb' as this is the required format for the transfer learning architectures.

In [32]:
# Set this to 'rgb' as this is the required format for the transfer learning architectures
color_mode = "rgb"
color_layers = 3
# Using the same size as before for the images
img_width, img_height = 48, 48
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32

# Training Data Augmentation for VGG16
train_datagen_vgg16 = ImageDataGenerator(
    preprocessing_function=preprocess_input_vgg16,  # Use model-specific preprocessing
    horizontal_flip=True,  # Faces are symmetric; flipping can simulate looking from another direction
    brightness_range=(0.5, 1.5),  # Randomly adjust brightness to simulate different lighting conditions
    shear_range=0.3,  # Shear transformations for perspective changes
    rotation_range=20,  # Slight rotation to introduce variability without distorting emotion features
    width_shift_range=0.1,  # Slight horizontal shifts to simulate off-center faces
    height_shift_range=0.1,  # Slight vertical shifts to account for different heights/angles
    zoom_range=0.1,  # Small zoom in/out to simulate closer or further away faces
)

# Training Data Augmentation for ResNet
train_datagen_resnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_resnetv2,  # Use model-specific preprocessing
    horizontal_flip=True,  # Faces are symmetric; flipping can simulate looking from another direction
    brightness_range=(0.5, 1.5),  # Randomly adjust brightness to simulate different lighting conditions
    shear_range=0.3,  # Shear transformations for perspective changes
    rotation_range=20,  # Slight rotation to introduce variability without distorting emotion features
    width_shift_range=0.1,  # Slight horizontal shifts to simulate off-center faces
    height_shift_range=0.1,  # Slight vertical shifts to account for different heights/angles
    zoom_range=0.1,  # Small zoom in/out to simulate closer or further away faces
)

# Training Data Augmentation for EfficientNet
train_datagen_efficientnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_efficientnetv2,  # Use model-specific preprocessing
    horizontal_flip=True,  # Faces are symmetric; flipping can simulate looking from another direction
    brightness_range=(0.5, 1.5),  # Randomly adjust brightness to simulate different lighting conditions
    shear_range=0.3,  # Shear transformations for perspective changes
    rotation_range=20,  # Slight rotation to introduce variability without distorting emotion features
    width_shift_range=0.1,  # Slight horizontal shifts to simulate off-center faces
    height_shift_range=0.1,  # Slight vertical shifts to account for different heights/angles
    zoom_range=0.1,  # Small zoom in/out to simulate closer or further away faces
)

# Validation and Testing Data should not be augmented! VGG16 version
validation_datagen_vgg16 = ImageDataGenerator(
    preprocessing_function=preprocess_input_vgg16
)  # Use model-specific preprocessing
test_datagen_vgg16 = ImageDataGenerator(
    preprocessing_function=preprocess_input_vgg16
)  # Use model-specific preprocessing

# Validation and Testing Data should not be augmented! ResNet version
validation_datagen_resnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_resnetv2
)  # Use model-specific preprocessing
test_datagen_resnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_resnetv2
)  # Use model-specific preprocessing

# Validation and Testing Data should not be augmented! Efficient Net version
validation_datagen_efficientnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_efficientnetv2
)  # Use model-specific preprocessing
test_datagen_efficientnet = ImageDataGenerator(
    preprocessing_function=preprocess_input_efficientnetv2
)  # Use model-specific preprocessing


# Creating train_dir, validation_dir, and test_dir with the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])

# Train Generator VGG16
train_generator_vgg16 = train_datagen_vgg16.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
)

# Train Generator ResNet
train_generator_resnet = train_datagen_resnet.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
)

# Train Generator EfficientNet
train_generator_efficientnet = train_datagen_efficientnet.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
)

# Validation Generator VGG16
validation_generator_vgg16 = validation_datagen_vgg16.flow_from_directory(
    validation_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for evaluation
)

# Validation Generator ResNet
validation_generator_resnet = validation_datagen_resnet.flow_from_directory(
    validation_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for evaluation
)

# Validation Generator EfficientNet
validation_generator_efficientnet = validation_datagen_efficientnet.flow_from_directory(
    validation_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for evaluation
)

# Testing Generator VGG16
test_generator_vgg16 = test_datagen_vgg16.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for testing
)

# Testing Generator ResNet
test_generator_resnet = test_datagen_resnet.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for testing
)

# Testing Generator EfficientNet
test_generator_efficientnet = test_datagen_efficientnet.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 15109 images belonging to 4 classes.
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Found 128 images belonging to 4 classes.
Found 128 images belonging to 4 classes.

VGG16 Model¶

Importing the VGG16 Architecture¶

In [33]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [34]:
vgg_model = VGG16(weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers))
vgg_model.summary()
Model: "vgg16"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer (InputLayer)        │ (None, 48, 48, 3)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block1_conv1 (Conv2D)           │ (None, 48, 48, 64)     │         1,792 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block1_conv2 (Conv2D)           │ (None, 48, 48, 64)     │        36,928 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block1_pool (MaxPooling2D)      │ (None, 24, 24, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block2_conv1 (Conv2D)           │ (None, 24, 24, 128)    │        73,856 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block2_conv2 (Conv2D)           │ (None, 24, 24, 128)    │       147,584 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block2_pool (MaxPooling2D)      │ (None, 12, 12, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block3_conv1 (Conv2D)           │ (None, 12, 12, 256)    │       295,168 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block3_conv2 (Conv2D)           │ (None, 12, 12, 256)    │       590,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block3_conv3 (Conv2D)           │ (None, 12, 12, 256)    │       590,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block3_pool (MaxPooling2D)      │ (None, 6, 6, 256)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block4_conv1 (Conv2D)           │ (None, 6, 6, 512)      │     1,180,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block4_conv2 (Conv2D)           │ (None, 6, 6, 512)      │     2,359,808 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block4_conv3 (Conv2D)           │ (None, 6, 6, 512)      │     2,359,808 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block4_pool (MaxPooling2D)      │ (None, 3, 3, 512)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block5_conv1 (Conv2D)           │ (None, 3, 3, 512)      │     2,359,808 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block5_conv2 (Conv2D)           │ (None, 3, 3, 512)      │     2,359,808 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block5_conv3 (Conv2D)           │ (None, 3, 3, 512)      │     2,359,808 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ block5_pool (MaxPooling2D)      │ (None, 1, 1, 512)      │             0 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 14,714,688 (56.13 MB)
 Trainable params: 14,714,688 (56.13 MB)
 Non-trainable params: 0 (0.00 B)

Model Building¶

  • Import VGG16 upto the layer of your choice and add Fully Connected layers on top of it.
In [35]:
# Define a new model that cuts VGG16 at the 'block3_pool' layer
model_output = vgg_model.get_layer("block3_pool").output
cut_model = Model(inputs=vgg_model.input, outputs=model_output)

for layer in vgg_model.layers:
    layer.trainable = False
In [36]:
new_vgg16_model = Sequential()

# Adding the convolutional part of the VGG16 model from above
new_vgg16_model.add(cut_model)

# Reduces each feature map to a single value by averaging all elements
new_vgg16_model.add(GlobalAveragePooling2D())

# Adding full connected layers
new_vgg16_model.add(Dense(512, activation="relu"))
new_vgg16_model.add(Dense(128, activation="relu"))
new_vgg16_model.add(Dense(64))
new_vgg16_model.add(BatchNormalization())
new_vgg16_model.add(ReLU())  # Using ReLU after batch normalization

# Adding output layer
new_vgg16_model.add(Dense(4, activation="softmax"))

# Using RMSprop Optimizer
optimizer = RMSprop(learning_rate=0.001)

Compiling and Training the VGG16 Model¶

In [37]:
new_vgg16_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_vgg16_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ functional_1 (Functional)       │ ?                      │     1,735,488 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d        │ ?                      │   0 (unbuilt) │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ ?                      │   0 (unbuilt) │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu (ReLU)                    │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ ?                      │   0 (unbuilt) │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 1,735,488 (6.62 MB)
 Trainable params: 0 (0.00 B)
 Non-trainable params: 1,735,488 (6.62 MB)
In [38]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

# Define the saving the best model callback
mc = ModelCheckpoint(
    f"{results_path}/best_model_vgg16_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 40 epochs and using validation set
history_vgg = new_vgg16_model.fit(
    train_generator_vgg16,
    epochs=40,
    validation_data=validation_generator_vgg16,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
I0000 00:00:1712794452.789847 1490572 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 40 bytes spill stores, 40 bytes spill loads

I0000 00:00:1712794452.792128 1490580 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 4 bytes spill stores, 4 bytes spill loads

I0000 00:00:1712794453.017261 1490573 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_473', 8 bytes spill stores, 8 bytes spill loads

I0000 00:00:1712794453.113233 1490576 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_1155', 32 bytes spill stores, 32 bytes spill loads

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Epoch 1: val_accuracy improved from -inf to 0.44183, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 17s 29ms/step - accuracy: 0.4447 - loss: 1.2222 - val_accuracy: 0.4418 - val_loss: 1.3317 - learning_rate: 0.0010
Epoch 2/40
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Epoch 2: val_accuracy improved from 0.44183 to 0.46373, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

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Epoch 3/40
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Epoch 3: val_accuracy improved from 0.46373 to 0.56982, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5495 - loss: 1.0414 - val_accuracy: 0.5698 - val_loss: 1.0627 - learning_rate: 0.0010
Epoch 4/40
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181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5889 - loss: 0.9940

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5887 - loss: 0.9942

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5886 - loss: 0.9942

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5885 - loss: 0.9943

191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5883 - loss: 0.9944

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5882 - loss: 0.9945

195/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5881 - loss: 0.9946

198/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5879 - loss: 0.9947

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5877 - loss: 0.9948

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5875 - loss: 0.9950

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5873 - loss: 0.9951

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5871 - loss: 0.9952

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5869 - loss: 0.9953

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5867 - loss: 0.9955

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5866 - loss: 0.9956

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5864 - loss: 0.9957

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5862 - loss: 0.9958

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5861 - loss: 0.9960

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5859 - loss: 0.9961

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5857 - loss: 0.9962

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5856 - loss: 0.9964

239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5854 - loss: 0.9965

242/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5853 - loss: 0.9966

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5852 - loss: 0.9967

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5851 - loss: 0.9968

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5850 - loss: 0.9969

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5849 - loss: 0.9970

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5847 - loss: 0.9971

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5846 - loss: 0.9971

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5845 - loss: 0.9972

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5843 - loss: 0.9973

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5842 - loss: 0.9975

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5840 - loss: 0.9976

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5839 - loss: 0.9977

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5838 - loss: 0.9978

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5836 - loss: 0.9979

282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5835 - loss: 0.9981

285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5833 - loss: 0.9982

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5832 - loss: 0.9983

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5831 - loss: 0.9984

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5829 - loss: 0.9985

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5828 - loss: 0.9985

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5828 - loss: 0.9986

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5826 - loss: 0.9987

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5825 - loss: 0.9988

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5824 - loss: 0.9989

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5823 - loss: 0.9989

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5821 - loss: 0.9990

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5820 - loss: 0.9991

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5819 - loss: 0.9992

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5818 - loss: 0.9993

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5817 - loss: 0.9994

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5816 - loss: 0.9995

331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5815 - loss: 0.9996

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5814 - loss: 0.9997

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5812 - loss: 0.9998

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5811 - loss: 1.0000

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5810 - loss: 1.0001

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5809 - loss: 1.0002

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 1.0003

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 1.0004

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5806 - loss: 1.0006

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5805 - loss: 1.0007

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5804 - loss: 1.0008

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5803 - loss: 1.0009

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5802 - loss: 1.0010

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5801 - loss: 1.0012

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5800 - loss: 1.0013

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5799 - loss: 1.0014

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5798 - loss: 1.0016

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5797 - loss: 1.0016

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5796 - loss: 1.0018

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5795 - loss: 1.0019

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5794 - loss: 1.0020

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5793 - loss: 1.0021

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5792 - loss: 1.0023

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5791 - loss: 1.0023

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5791 - loss: 1.0024

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5790 - loss: 1.0026

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 1.0027

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 1.0028

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5787 - loss: 1.0029

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5786 - loss: 1.0031

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0032

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5784 - loss: 1.0033

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5783 - loss: 1.0034

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0035

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5781 - loss: 1.0038

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5780 - loss: 1.0039

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5779 - loss: 1.0040

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5779 - loss: 1.0041

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5778 - loss: 1.0041

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5777 - loss: 1.0042

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5777 - loss: 1.0043

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5776 - loss: 1.0044

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5775 - loss: 1.0045

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5775 - loss: 1.0046

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5774 - loss: 1.0047

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5773 - loss: 1.0047
Epoch 4: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5772 - loss: 1.0050 - val_accuracy: 0.4676 - val_loss: 1.1357 - learning_rate: 0.0010
Epoch 5/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:32 197ms/step - accuracy: 0.5000 - loss: 0.9534

  4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5020 - loss: 1.0413  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5090 - loss: 1.0456 

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5172 - loss: 1.0408

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5256 - loss: 1.0326

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5298 - loss: 1.0310

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5334 - loss: 1.0299

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5367 - loss: 1.0294

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5388 - loss: 1.0303

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5414 - loss: 1.0294

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5434 - loss: 1.0285

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5455 - loss: 1.0274

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5486 - loss: 1.0259

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5510 - loss: 1.0253

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5527 - loss: 1.0254

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5544 - loss: 1.0250

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5560 - loss: 1.0246

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5575 - loss: 1.0246

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5587 - loss: 1.0249

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5596 - loss: 1.0250

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5605 - loss: 1.0251

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429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0268

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0267

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0267

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0266

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0265

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0265

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0264

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5654 - loss: 1.0264

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5654 - loss: 1.0263

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5654 - loss: 1.0263

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5655 - loss: 1.0262

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5655 - loss: 1.0262

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5655 - loss: 1.0261

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5655 - loss: 1.0261
Epoch 5: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5655 - loss: 1.0259 - val_accuracy: 0.4173 - val_loss: 1.4831 - learning_rate: 0.0010
Epoch 6/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 170ms/step - accuracy: 0.4062 - loss: 1.2991

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4688 - loss: 1.1953   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4862 - loss: 1.1581

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5006 - loss: 1.1296

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5092 - loss: 1.1114

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5126 - loss: 1.1042

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5178 - loss: 1.0945

 21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5219 - loss: 1.0871

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5265 - loss: 1.0800 

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5302 - loss: 1.0736

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5336 - loss: 1.0676

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5364 - loss: 1.0628

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5388 - loss: 1.0585

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5411 - loss: 1.0545

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5432 - loss: 1.0509

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5443 - loss: 1.0492

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5455 - loss: 1.0477

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5471 - loss: 1.0458

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5485 - loss: 1.0440

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5497 - loss: 1.0423

 58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5508 - loss: 1.0409

 61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5518 - loss: 1.0396

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5529 - loss: 1.0384

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5539 - loss: 1.0372

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5549 - loss: 1.0360

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5558 - loss: 1.0346

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5567 - loss: 1.0333

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5575 - loss: 1.0321

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5582 - loss: 1.0311

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5588 - loss: 1.0302

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5594 - loss: 1.0294

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5600 - loss: 1.0286

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5606 - loss: 1.0278

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5612 - loss: 1.0271

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5616 - loss: 1.0265

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5621 - loss: 1.0260

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5624 - loss: 1.0255

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5627 - loss: 1.0251

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5629 - loss: 1.0247

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5632 - loss: 1.0244

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5634 - loss: 1.0241

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5636 - loss: 1.0238

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5638 - loss: 1.0235

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5640 - loss: 1.0232

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5642 - loss: 1.0230

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5643 - loss: 1.0227

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5645 - loss: 1.0224

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5647 - loss: 1.0222

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5648 - loss: 1.0220

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5649 - loss: 1.0217

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5651 - loss: 1.0215

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5652 - loss: 1.0213

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5653 - loss: 1.0210

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5654 - loss: 1.0208

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5655 - loss: 1.0206

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5656 - loss: 1.0204

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0203

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0201

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0199

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0196

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0193

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0190

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0187

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5666 - loss: 1.0184

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5667 - loss: 1.0181

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5668 - loss: 1.0178

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5669 - loss: 1.0175

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5670 - loss: 1.0173

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5671 - loss: 1.0171

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5672 - loss: 1.0169

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5672 - loss: 1.0168

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5673 - loss: 1.0166

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5673 - loss: 1.0164

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5674 - loss: 1.0162

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5674 - loss: 1.0161

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5675 - loss: 1.0159

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5675 - loss: 1.0158

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5676 - loss: 1.0157

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5677 - loss: 1.0155

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5677 - loss: 1.0154

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0153

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5677 - loss: 1.0151

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0150

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0149

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5678 - loss: 1.0148

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0147

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0146

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0145

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0145

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5679 - loss: 1.0144

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0144

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0143

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0142

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0142

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0141

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0141

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5680 - loss: 1.0140

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0140

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0139

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0139

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0138

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0137

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0137

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5681 - loss: 1.0136

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0136

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0135

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0135

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0134

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5682 - loss: 1.0134

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0133

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0133

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0132

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0132

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0131

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0130

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0130

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5684 - loss: 1.0129

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0129

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0128

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0128

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5685 - loss: 1.0127

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0127

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0127

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0126

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0126

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5686 - loss: 1.0125

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5687 - loss: 1.0125

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0125

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5687 - loss: 1.0124

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0122

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0123

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0123

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5688 - loss: 1.0123

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5689 - loss: 1.0123

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5689 - loss: 1.0123
Epoch 6: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5689 - loss: 1.0123 - val_accuracy: 0.5268 - val_loss: 1.0536 - learning_rate: 0.0010
Epoch 7/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 168ms/step - accuracy: 0.5312 - loss: 1.0298

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5111 - loss: 1.0671   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5241 - loss: 1.0474

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5374 - loss: 1.0332

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5456 - loss: 1.0250

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5499 - loss: 1.0199

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5519 - loss: 1.0170

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5560 - loss: 1.0133

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5587 - loss: 1.0106

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5599 - loss: 1.0096

 28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5607 - loss: 1.0089

 31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5616 - loss: 1.0080

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5622 - loss: 1.0068

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5627 - loss: 1.0057

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5632 - loss: 1.0046

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5635 - loss: 1.0042

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5638 - loss: 1.0039

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5641 - loss: 1.0038

 50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5645 - loss: 1.0035

 53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5645 - loss: 1.0036

 56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5646 - loss: 1.0037

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5646 - loss: 1.0041

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5647 - loss: 1.0045

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5648 - loss: 1.0046

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0045

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5651 - loss: 1.0046

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5652 - loss: 1.0046

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5652 - loss: 1.0046

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5651 - loss: 1.0046

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5651 - loss: 1.0046

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0046

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0047

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0048

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0049

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0051

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5649 - loss: 1.0052

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0053

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0054

105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5650 - loss: 1.0054

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0055

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0055

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5650 - loss: 1.0056

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0056

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0056

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5651 - loss: 1.0055

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5651 - loss: 1.0054

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0053

137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0053

140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5653 - loss: 1.0052

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5654 - loss: 1.0051

145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5655 - loss: 1.0050

148/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5656 - loss: 1.0049

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5656 - loss: 1.0047

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0046

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5658 - loss: 1.0046

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5658 - loss: 1.0045

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0044

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0043

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0042

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0041

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0040

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0040

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0040

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0040

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238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5671 - loss: 1.0042

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285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5678 - loss: 1.0037

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305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5680 - loss: 1.0037

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368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0037

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5679 - loss: 1.0037

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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0041

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0041

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5679 - loss: 1.0042
Epoch 7: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5679 - loss: 1.0042 - val_accuracy: 0.4266 - val_loss: 1.3867 - learning_rate: 0.0010
Epoch 8/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.6562 - loss: 0.9789

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6530 - loss: 0.9758   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6320 - loss: 0.9878

  9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6263 - loss: 0.9901

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6179 - loss: 0.9954

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6087 - loss: 0.9991

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6019 - loss: 1.0019

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5968 - loss: 1.0023

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5930 - loss: 1.0016

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5907 - loss: 1.0007

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9992

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5877 - loss: 0.9982

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5865 - loss: 0.9979

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5854 - loss: 0.9977

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5848 - loss: 0.9969

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5842 - loss: 0.9966

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5837 - loss: 0.9964

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5832 - loss: 0.9964

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5830 - loss: 0.9961

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9957

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9953

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9951

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9947

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5827 - loss: 0.9945

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5823 - loss: 0.9946

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5821 - loss: 0.9947

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5817 - loss: 0.9950

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451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5767 - loss: 0.9962

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5766 - loss: 0.9962
Epoch 8: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5765 - loss: 0.9963 - val_accuracy: 0.4535 - val_loss: 1.2874 - learning_rate: 0.0010
Epoch 9/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.5938 - loss: 0.8266

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5866 - loss: 0.8793   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5740 - loss: 0.9336

  9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5718 - loss: 0.9489

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5729 - loss: 0.9609

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5765 - loss: 0.9611

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5774 - loss: 0.9639

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5772 - loss: 0.9670

 22/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5776 - loss: 0.9692

 24/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5778 - loss: 0.9714

 27/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5777 - loss: 0.9756

 30/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5781 - loss: 0.9787

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5790 - loss: 0.9805 

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5794 - loss: 0.9823

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5798 - loss: 0.9828

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5807 - loss: 0.9831

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5815 - loss: 0.9831

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5821 - loss: 0.9830

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5830 - loss: 0.9828

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5838 - loss: 0.9827

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5844 - loss: 0.9826

 58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5849 - loss: 0.9824

 61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5854 - loss: 0.9825

 64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5856 - loss: 0.9827

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5858 - loss: 0.9830

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5859 - loss: 0.9835

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5860 - loss: 0.9837

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5861 - loss: 0.9837

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5861 - loss: 0.9839

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5860 - loss: 0.9841

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5860 - loss: 0.9844

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5860 - loss: 0.9846

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5859 - loss: 0.9847

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5859 - loss: 0.9849

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5860 - loss: 0.9849

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5860 - loss: 0.9848

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5860 - loss: 0.9848

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5860 - loss: 0.9848

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5859 - loss: 0.9849

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5858 - loss: 0.9849

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5858 - loss: 0.9848

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5857 - loss: 0.9848

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5857 - loss: 0.9847

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5856 - loss: 0.9847

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5856 - loss: 0.9847

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5855 - loss: 0.9846

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5855 - loss: 0.9846

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5854 - loss: 0.9846

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5854 - loss: 0.9846

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5853 - loss: 0.9846

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5853 - loss: 0.9845

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9845

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9844

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9844

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9843

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9842

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9842

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9842

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9841

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9841

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9841

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9841

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9841

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9841

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9841

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9841

189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5851 - loss: 0.9842

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5851 - loss: 0.9842

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5850 - loss: 0.9842

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5850 - loss: 0.9843

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5849 - loss: 0.9844

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5848 - loss: 0.9845

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5848 - loss: 0.9845

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5847 - loss: 0.9846

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5846 - loss: 0.9847

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5845 - loss: 0.9848

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5844 - loss: 0.9850

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5843 - loss: 0.9851

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5843 - loss: 0.9852

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5841 - loss: 0.9853

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5841 - loss: 0.9853

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5840 - loss: 0.9854

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5839 - loss: 0.9855

234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5838 - loss: 0.9857

237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5837 - loss: 0.9857

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5836 - loss: 0.9858

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5835 - loss: 0.9859

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5834 - loss: 0.9860

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5833 - loss: 0.9861

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5832 - loss: 0.9861

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5831 - loss: 0.9862

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5830 - loss: 0.9863

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5829 - loss: 0.9864

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5829 - loss: 0.9865

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5828 - loss: 0.9865

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5827 - loss: 0.9866

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5826 - loss: 0.9867

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5825 - loss: 0.9868

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5824 - loss: 0.9869

282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5823 - loss: 0.9869

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5823 - loss: 0.9870

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5822 - loss: 0.9871

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5821 - loss: 0.9871

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5821 - loss: 0.9872

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5820 - loss: 0.9873

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5819 - loss: 0.9874

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5818 - loss: 0.9874

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5818 - loss: 0.9875

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5817 - loss: 0.9876

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5816 - loss: 0.9877

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5815 - loss: 0.9877

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5815 - loss: 0.9878

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5814 - loss: 0.9879

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5813 - loss: 0.9879

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5813 - loss: 0.9880

330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5812 - loss: 0.9880

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5812 - loss: 0.9881

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5811 - loss: 0.9881

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5811 - loss: 0.9881

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5810 - loss: 0.9882

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5810 - loss: 0.9882

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5809 - loss: 0.9882

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5809 - loss: 0.9883

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5809 - loss: 0.9883

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 0.9883

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 0.9883

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 0.9883

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5808 - loss: 0.9884

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 0.9884

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 0.9884

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5807 - loss: 0.9885

377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5806 - loss: 0.9885

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5806 - loss: 0.9886

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5805 - loss: 0.9886

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5805 - loss: 0.9887

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9887

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9888

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5804 - loss: 0.9888

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9888

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9888

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5803 - loss: 0.9889

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9889

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9889

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5802 - loss: 0.9890

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5801 - loss: 0.9890

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5800 - loss: 0.9891

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5800 - loss: 0.9891

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5800 - loss: 0.9891

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5799 - loss: 0.9892

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5798 - loss: 0.9892

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5798 - loss: 0.9893

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9893

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9893

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5797 - loss: 0.9894

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5796 - loss: 0.9894

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5795 - loss: 0.9895
Epoch 9: val_accuracy did not improve from 0.56982

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5795 - loss: 0.9895 - val_accuracy: 0.5329 - val_loss: 1.4058 - learning_rate: 0.0010
Epoch 10/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.6562 - loss: 0.8816

  3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 31ms/step - accuracy: 0.6372 - loss: 0.8518  

  5/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.6017 - loss: 0.8977

  8/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5844 - loss: 0.9205

 11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5775 - loss: 0.9351

 14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5759 - loss: 0.9412

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5759 - loss: 0.9437

 20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5769 - loss: 0.9452

 23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5784 - loss: 0.9455

 26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5789 - loss: 0.9474

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5786 - loss: 0.9499 

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5780 - loss: 0.9527

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5768 - loss: 0.9556

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5758 - loss: 0.9577

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5753 - loss: 0.9598

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5748 - loss: 0.9621

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5747 - loss: 0.9635

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5746 - loss: 0.9646

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5746 - loss: 0.9662

 54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5747 - loss: 0.9674

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5746 - loss: 0.9686

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5746 - loss: 0.9694

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5747 - loss: 0.9700

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5749 - loss: 0.9704

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5753 - loss: 0.9707

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5756 - loss: 0.9712

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5758 - loss: 0.9716

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5760 - loss: 0.9721

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5762 - loss: 0.9725

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5763 - loss: 0.9728

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5764 - loss: 0.9732

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5766 - loss: 0.9738

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5767 - loss: 0.9744

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5769 - loss: 0.9748

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5770 - loss: 0.9754

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5772 - loss: 0.9760

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5773 - loss: 0.9766

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5775 - loss: 0.9769

106/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5776 - loss: 0.9771

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5779 - loss: 0.9774

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5781 - loss: 0.9777

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5784 - loss: 0.9780

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5786 - loss: 0.9783

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5788 - loss: 0.9786

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5790 - loss: 0.9788

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5792 - loss: 0.9790

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5794 - loss: 0.9791

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5797 - loss: 0.9793

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5798 - loss: 0.9794

137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5800 - loss: 0.9795

140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5803 - loss: 0.9797

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5805 - loss: 0.9798

145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5807 - loss: 0.9799

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5808 - loss: 0.9799

149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5809 - loss: 0.9800

152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5812 - loss: 0.9802

155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5814 - loss: 0.9803

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5816 - loss: 0.9803

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5818 - loss: 0.9804

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5820 - loss: 0.9805

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5822 - loss: 0.9806

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5823 - loss: 0.9807

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5825 - loss: 0.9809

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5826 - loss: 0.9810

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5827 - loss: 0.9811

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5828 - loss: 0.9812

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5830 - loss: 0.9813

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5831 - loss: 0.9815

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5832 - loss: 0.9815

190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5833 - loss: 0.9816

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5834 - loss: 0.9817

196/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5835 - loss: 0.9818

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Epoch 10: val_accuracy improved from 0.56982 to 0.58409, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5870 - loss: 0.9811 - val_accuracy: 0.5841 - val_loss: 0.9809 - learning_rate: 0.0010
Epoch 11/40
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441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9881

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9880

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 0.9880
Epoch 11: val_accuracy did not improve from 0.58409

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5783 - loss: 0.9880 - val_accuracy: 0.4774 - val_loss: 1.3026 - learning_rate: 0.0010
Epoch 12/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.5938 - loss: 0.8676

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5658 - loss: 0.9366   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5664 - loss: 0.9493

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5676 - loss: 0.9588

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5697 - loss: 0.9601

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5759 - loss: 0.9555

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5785 - loss: 0.9549

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5784 - loss: 0.9572 

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5772 - loss: 0.9614

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5761 - loss: 0.9651

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5758 - loss: 0.9681

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5756 - loss: 0.9697

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5752 - loss: 0.9719

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5748 - loss: 0.9739

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5745 - loss: 0.9757

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5744 - loss: 0.9768

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5745 - loss: 0.9781

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5746 - loss: 0.9790

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5745 - loss: 0.9802

 54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5743 - loss: 0.9815

 57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5741 - loss: 0.9829

 59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5739 - loss: 0.9838

 61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5738 - loss: 0.9847

 64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5737 - loss: 0.9857

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5738 - loss: 0.9864

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5738 - loss: 0.9871

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5739 - loss: 0.9878

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5739 - loss: 0.9884

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5739 - loss: 0.9887

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5739 - loss: 0.9891

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5740 - loss: 0.9895

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5741 - loss: 0.9898

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5741 - loss: 0.9900

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5742 - loss: 0.9902

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5744 - loss: 0.9902

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5746 - loss: 0.9902

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5748 - loss: 0.9902

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5751 - loss: 0.9901

104/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5752 - loss: 0.9900

107/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5754 - loss: 0.9898

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5757 - loss: 0.9896

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9894

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9892

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5763 - loss: 0.9890

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5766 - loss: 0.9889

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5768 - loss: 0.9887

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5770 - loss: 0.9886

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5771 - loss: 0.9886

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5773 - loss: 0.9886

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5774 - loss: 0.9885

137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5775 - loss: 0.9885

140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5777 - loss: 0.9885

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5778 - loss: 0.9885

146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5779 - loss: 0.9885

149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5780 - loss: 0.9886

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9886

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9887

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9888

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9889

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9890

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9890

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5781 - loss: 0.9890

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9891

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9891

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9892

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9892

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9893

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9894

191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9895

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5781 - loss: 0.9895

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9896

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9896

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9897

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9898

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9898

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9899

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9899

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5779 - loss: 0.9899

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5779 - loss: 0.9899

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9899

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9899

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9898

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5780 - loss: 0.9898

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9897

235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9897

237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5781 - loss: 0.9896

239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5781 - loss: 0.9896

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9895

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9895

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9894

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9894

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5782 - loss: 0.9894

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5782 - loss: 0.9894

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5782 - loss: 0.9893

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9893

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9892

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9892

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9892

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9891

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5783 - loss: 0.9891

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5784 - loss: 0.9891

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5784 - loss: 0.9891

282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5784 - loss: 0.9890

285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5784 - loss: 0.9890

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9890

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9889

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9888

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9888

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5784 - loss: 0.9888

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5785 - loss: 0.9888

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5785 - loss: 0.9888

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5785 - loss: 0.9887

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5785 - loss: 0.9887

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5785 - loss: 0.9887

331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5786 - loss: 0.9887

334/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5786 - loss: 0.9886

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5786 - loss: 0.9886

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5786 - loss: 0.9886

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5787 - loss: 0.9885

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5787 - loss: 0.9885

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5787 - loss: 0.9885

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5788 - loss: 0.9884

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5788 - loss: 0.9883

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9883

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9882

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5789 - loss: 0.9882

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9881

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9881

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9880

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5790 - loss: 0.9880

378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5791 - loss: 0.9880

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5791 - loss: 0.9879

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5791 - loss: 0.9879

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9879

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9878

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5792 - loss: 0.9878

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9878

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9877

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5793 - loss: 0.9877

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9876

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9876

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5794 - loss: 0.9875

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9875

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9874

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5795 - loss: 0.9874

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5796 - loss: 0.9873

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5797 - loss: 0.9872

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5797 - loss: 0.9872

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5797 - loss: 0.9872

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9871

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5798 - loss: 0.9870

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9870

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9869

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5799 - loss: 0.9869

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9869

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5800 - loss: 0.9868

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5801 - loss: 0.9867
Epoch 12: val_accuracy did not improve from 0.58409

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5802 - loss: 0.9866 - val_accuracy: 0.5525 - val_loss: 1.1757 - learning_rate: 0.0010
Epoch 13/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 179ms/step - accuracy: 0.5938 - loss: 0.9203

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5677 - loss: 0.9651   

  6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5554 - loss: 0.9847

  8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5524 - loss: 0.9887

 11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5526 - loss: 0.9916

 14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5528 - loss: 0.9950

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5535 - loss: 0.9971

 19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5537 - loss: 0.9981

 21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5543 - loss: 0.9991

 23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5554 - loss: 0.9993

 26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5569 - loss: 1.0002

 29/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5588 - loss: 0.9997

 32/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5606 - loss: 0.9986

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5621 - loss: 0.9974 

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5633 - loss: 0.9965

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5644 - loss: 0.9959

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5656 - loss: 0.9956

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5666 - loss: 0.9954

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5673 - loss: 0.9952

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5682 - loss: 0.9949

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5692 - loss: 0.9944

 57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5698 - loss: 0.9942

 59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5703 - loss: 0.9939

 62/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5712 - loss: 0.9931

 64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5717 - loss: 0.9927

 66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5720 - loss: 0.9923

 68/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5723 - loss: 0.9920

 70/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5726 - loss: 0.9916

 73/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5729 - loss: 0.9911

 76/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5732 - loss: 0.9906

 78/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5735 - loss: 0.9903

 81/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5737 - loss: 0.9899

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5741 - loss: 0.9895

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5742 - loss: 0.9893

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5744 - loss: 0.9891

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5746 - loss: 0.9889

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5748 - loss: 0.9886

 97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5750 - loss: 0.9883

100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5751 - loss: 0.9883

103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5753 - loss: 0.9882

105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5753 - loss: 0.9881

108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5754 - loss: 0.9881

111/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5755 - loss: 0.9880

114/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5756 - loss: 0.9879

117/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.5757 - loss: 0.9878

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.5758 - loss: 0.9877

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.5759 - loss: 0.9876

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5760 - loss: 0.9876

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9876

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9875

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5762 - loss: 0.9873

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5763 - loss: 0.9873

146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5764 - loss: 0.9872

149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5765 - loss: 0.9871

152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5767 - loss: 0.9870

155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5768 - loss: 0.9869

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5769 - loss: 0.9868

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5771 - loss: 0.9867

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5772 - loss: 0.9866

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5773 - loss: 0.9865

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5774 - loss: 0.9865

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5774 - loss: 0.9865

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5775 - loss: 0.9864

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5776 - loss: 0.9864

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5776 - loss: 0.9864

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5777 - loss: 0.9864

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5777 - loss: 0.9864

189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5778 - loss: 0.9864

192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5778 - loss: 0.9864

195/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5779 - loss: 0.9863

198/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5780 - loss: 0.9863

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5780 - loss: 0.9863

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5781 - loss: 0.9863

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5781 - loss: 0.9862

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5782 - loss: 0.9862

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5782 - loss: 0.9861

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5783 - loss: 0.9861

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5783 - loss: 0.9861

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5784 - loss: 0.9861

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5784 - loss: 0.9860

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5785 - loss: 0.9860

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5785 - loss: 0.9860

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9860

235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9859

238/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5786 - loss: 0.9859

241/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9858

243/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9858

245/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5787 - loss: 0.9857

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5788 - loss: 0.9857

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5788 - loss: 0.9856

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5789 - loss: 0.9855

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5789 - loss: 0.9855

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5790 - loss: 0.9854

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5790 - loss: 0.9853

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9852

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9851

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5791 - loss: 0.9850

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5792 - loss: 0.9850

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5792 - loss: 0.9849

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9848

284/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9847

286/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5793 - loss: 0.9847

289/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5794 - loss: 0.9846

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5794 - loss: 0.9845

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5794 - loss: 0.9845

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5795 - loss: 0.9844

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5795 - loss: 0.9844

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5796 - loss: 0.9843

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5796 - loss: 0.9843

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9842

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9842

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5797 - loss: 0.9841

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9841

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9841

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5798 - loss: 0.9840

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5799 - loss: 0.9839

329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5799 - loss: 0.9839

332/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5800 - loss: 0.9838

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5800 - loss: 0.9837

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9837

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9837

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5801 - loss: 0.9836

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5802 - loss: 0.9836

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5802 - loss: 0.9835

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9834

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9834

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5803 - loss: 0.9833

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9833

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9832

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5804 - loss: 0.9832

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5805 - loss: 0.9831

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9830

378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9830

380/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5806 - loss: 0.9829

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5806 - loss: 0.9829

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9828

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9828

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5807 - loss: 0.9827

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9827

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9826

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9825

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5808 - loss: 0.9825

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9824

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9824

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5809 - loss: 0.9823

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9822

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9822

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9821

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5810 - loss: 0.9821

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.5811 - loss: 0.9820

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5811 - loss: 0.9819

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5811 - loss: 0.9819

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9818

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9818

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5812 - loss: 0.9817

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9816

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9816

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5813 - loss: 0.9815

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9815

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9814

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5814 - loss: 0.9813

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5815 - loss: 0.9813

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.5815 - loss: 0.9813
Epoch 13: val_accuracy did not improve from 0.58409

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5815 - loss: 0.9812 - val_accuracy: 0.3514 - val_loss: 1.7125 - learning_rate: 0.0010
Epoch 14/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 167ms/step - accuracy: 0.5000 - loss: 1.4017

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5254 - loss: 1.2209   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5338 - loss: 1.1648

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5320 - loss: 1.1506

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5291 - loss: 1.1376

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5318 - loss: 1.1239

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5331 - loss: 1.1142 

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5341 - loss: 1.1056

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5364 - loss: 1.0957

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5383 - loss: 1.0867

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5400 - loss: 1.0786

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5412 - loss: 1.0722

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5423 - loss: 1.0667

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5432 - loss: 1.0621

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0580

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5451 - loss: 1.0541

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5463 - loss: 1.0504

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407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 0.9843

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 0.9842

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 0.9842

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 0.9841

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 0.9841

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 0.9840

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 0.9840

426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5790 - loss: 0.9839

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5790 - loss: 0.9839

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5790 - loss: 0.9838

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5790 - loss: 0.9838

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9837

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9837

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9836

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5791 - loss: 0.9836

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9836

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5792 - loss: 0.9835

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9834

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9834

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5793 - loss: 0.9833
Epoch 14: val_accuracy did not improve from 0.58409

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5793 - loss: 0.9833 - val_accuracy: 0.5754 - val_loss: 0.9672 - learning_rate: 0.0010
Epoch 15/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.6250 - loss: 0.8782

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6445 - loss: 0.8743   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6353 - loss: 0.9044

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6343 - loss: 0.9076

 11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6337 - loss: 0.9085

 14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6295 - loss: 0.9135

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6273 - loss: 0.9151

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6244 - loss: 0.9172 

 23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6222 - loss: 0.9200

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6197 - loss: 0.9239

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6177 - loss: 0.9273

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6161 - loss: 0.9298

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6144 - loss: 0.9316

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6130 - loss: 0.9333

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6125 - loss: 0.9341

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.9351

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6110 - loss: 0.9362

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6104 - loss: 0.9370

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6098 - loss: 0.9377

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6091 - loss: 0.9386

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6084 - loss: 0.9396

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6076 - loss: 0.9407

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6069 - loss: 0.9416

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6062 - loss: 0.9426

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6056 - loss: 0.9434

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6051 - loss: 0.9442

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6046 - loss: 0.9450

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6042 - loss: 0.9458

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6038 - loss: 0.9464

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6034 - loss: 0.9470

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6032 - loss: 0.9474

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6029 - loss: 0.9479

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6026 - loss: 0.9482

 95/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6022 - loss: 0.9487

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6019 - loss: 0.9492

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6015 - loss: 0.9495

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6012 - loss: 0.9499

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6010 - loss: 0.9501

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6007 - loss: 0.9505

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6004 - loss: 0.9508

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6002 - loss: 0.9510

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6001 - loss: 0.9511

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5998 - loss: 0.9513

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5997 - loss: 0.9514

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5994 - loss: 0.9516

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5993 - loss: 0.9518

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5991 - loss: 0.9521

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5989 - loss: 0.9523

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5986 - loss: 0.9526

139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5984 - loss: 0.9529

142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5982 - loss: 0.9531

145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5980 - loss: 0.9533

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5977 - loss: 0.9535

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5975 - loss: 0.9538

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5973 - loss: 0.9541

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5970 - loss: 0.9543

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5969 - loss: 0.9545

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5967 - loss: 0.9547

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5966 - loss: 0.9550

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5964 - loss: 0.9551

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5963 - loss: 0.9554

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5961 - loss: 0.9556

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5960 - loss: 0.9558

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5959 - loss: 0.9560

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5958 - loss: 0.9562

187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5956 - loss: 0.9565

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5955 - loss: 0.9567

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5954 - loss: 0.9569

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5952 - loss: 0.9571

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5951 - loss: 0.9573

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5949 - loss: 0.9576

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5948 - loss: 0.9577

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5947 - loss: 0.9579

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5946 - loss: 0.9581

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5945 - loss: 0.9582

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5945 - loss: 0.9583

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5944 - loss: 0.9585

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9586

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9586

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5943 - loss: 0.9587

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9588

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9589

234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5942 - loss: 0.9589

237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5941 - loss: 0.9590

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9591

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9591

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9592

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5941 - loss: 0.9592

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9593

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9593

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9594

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9594

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9595

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9595

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5940 - loss: 0.9596

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9596

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9597

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5939 - loss: 0.9598

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Epoch 15: val_accuracy improved from 0.58409 to 0.61141, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5923 - loss: 0.9639 - val_accuracy: 0.6114 - val_loss: 0.9456 - learning_rate: 0.0010
Epoch 16/40
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163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5871 - loss: 0.9717

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5872 - loss: 0.9716

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5873 - loss: 0.9716

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5873 - loss: 0.9715

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5874 - loss: 0.9715

178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5874 - loss: 0.9715

181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5875 - loss: 0.9715

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5875 - loss: 0.9715

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190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5875 - loss: 0.9715

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5875 - loss: 0.9715

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5876 - loss: 0.9715

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5877 - loss: 0.9715

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9715

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9713

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9713

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5877 - loss: 0.9714

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9714

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9714

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5876 - loss: 0.9715

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5876 - loss: 0.9715

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9715

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5875 - loss: 0.9714

324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713

326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5875 - loss: 0.9713

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9713

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9713

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9712

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5876 - loss: 0.9711

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9711

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9711

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5877 - loss: 0.9710

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5877 - loss: 0.9710

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5877 - loss: 0.9710

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9710

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9709

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9708

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5878 - loss: 0.9708

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9708

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5879 - loss: 0.9707

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9707

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9708

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5879 - loss: 0.9708

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9708

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9708

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5878 - loss: 0.9709
Epoch 16: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5877 - loss: 0.9710 - val_accuracy: 0.6108 - val_loss: 0.9286 - learning_rate: 0.0010
Epoch 17/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.7500 - loss: 0.8049

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6921 - loss: 0.8329   

  6/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6791 - loss: 0.8501

  9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6700 - loss: 0.8632 

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6632 - loss: 0.8720

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6576 - loss: 0.8791

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6558 - loss: 0.8823

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6539 - loss: 0.8852 

 23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6515 - loss: 0.8879

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6487 - loss: 0.8905

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6457 - loss: 0.8937

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6433 - loss: 0.8962

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6419 - loss: 0.8979

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6406 - loss: 0.8999

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6382 - loss: 0.9035

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6355 - loss: 0.9074

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405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5965 - loss: 0.9622

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5964 - loss: 0.9622

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5964 - loss: 0.9623

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5963 - loss: 0.9624

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5963 - loss: 0.9625

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5962 - loss: 0.9625

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5961 - loss: 0.9626

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5961 - loss: 0.9627

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5960 - loss: 0.9627

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5960 - loss: 0.9628

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5959 - loss: 0.9629

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5958 - loss: 0.9629

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5958 - loss: 0.9630

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5957 - loss: 0.9631

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5957 - loss: 0.9631

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5956 - loss: 0.9632

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5956 - loss: 0.9632

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5955 - loss: 0.9633

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5955 - loss: 0.9633

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5954 - loss: 0.9634

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5954 - loss: 0.9634
Epoch 17: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5952 - loss: 0.9635 - val_accuracy: 0.5632 - val_loss: 0.9927 - learning_rate: 0.0010
Epoch 18/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 179ms/step - accuracy: 0.6562 - loss: 0.7410

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6328 - loss: 0.8021   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6234 - loss: 0.8275

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6181 - loss: 0.8490

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6150 - loss: 0.8608

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6121 - loss: 0.8710

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6078 - loss: 0.8840 

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6050 - loss: 0.8917

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6018 - loss: 0.8999

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6002 - loss: 0.9049

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5992 - loss: 0.9087

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5979 - loss: 0.9147

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5974 - loss: 0.9176

 36/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5972 - loss: 0.9200

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5966 - loss: 0.9231 

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5962 - loss: 0.9261

 45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5961 - loss: 0.9283

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5958 - loss: 0.9301

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5955 - loss: 0.9318

 53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5952 - loss: 0.9328

 56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5949 - loss: 0.9343

 59/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5945 - loss: 0.9360

 62/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5941 - loss: 0.9375

 64/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5938 - loss: 0.9386

 66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5935 - loss: 0.9396

 69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5929 - loss: 0.9411

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5925 - loss: 0.9424

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5922 - loss: 0.9435

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5921 - loss: 0.9443

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5921 - loss: 0.9450

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5920 - loss: 0.9454

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5919 - loss: 0.9459

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5917 - loss: 0.9466

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5916 - loss: 0.9474

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5915 - loss: 0.9480

 97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5914 - loss: 0.9486

100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5914 - loss: 0.9491

103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5913 - loss: 0.9497

106/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5912 - loss: 0.9502

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5912 - loss: 0.9507

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5912 - loss: 0.9512

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9516

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9521

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5910 - loss: 0.9525

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5910 - loss: 0.9528

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5910 - loss: 0.9531

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5910 - loss: 0.9534

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9537

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9538

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5911 - loss: 0.9541

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5912 - loss: 0.9543

144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5913 - loss: 0.9544

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5914 - loss: 0.9544

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5915 - loss: 0.9545

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5916 - loss: 0.9546

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5917 - loss: 0.9547

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5918 - loss: 0.9548

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5919 - loss: 0.9550

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5919 - loss: 0.9551

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5920 - loss: 0.9553

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5920 - loss: 0.9555

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9557

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9558

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9560

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9561

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9562

187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5921 - loss: 0.9563

190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5922 - loss: 0.9565

192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5922 - loss: 0.9565

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9567

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9568

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9569

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9570

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9571

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5922 - loss: 0.9572

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9573

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9574

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9575

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9576

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9577

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5923 - loss: 0.9579

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9580

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9580

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9581

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5924 - loss: 0.9582

239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5925 - loss: 0.9583

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5925 - loss: 0.9584

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5925 - loss: 0.9585

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9585

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9586

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9587

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9587

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9588

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9589

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9590

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9591

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9592

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9593

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9595

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5927 - loss: 0.9596

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9597

283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5926 - loss: 0.9597

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9598

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9599

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9600

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9601

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5926 - loss: 0.9602

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9602

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9603

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9604

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9604

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9605

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9605

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9606

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5925 - loss: 0.9607

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9607

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9608

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9608

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9609

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5925 - loss: 0.9609

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9610

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9611

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9611

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9612

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5924 - loss: 0.9612

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9613

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9613

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9614

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9615

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9615

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5923 - loss: 0.9616

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5923 - loss: 0.9616

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9617

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9618

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5922 - loss: 0.9619

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9620

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9621

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9621

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9622

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5921 - loss: 0.9623

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9623

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5920 - loss: 0.9624

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9625

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9625

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5920 - loss: 0.9626

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9626

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9626

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9627

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5919 - loss: 0.9628

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5918 - loss: 0.9629

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5918 - loss: 0.9629
Epoch 18: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5918 - loss: 0.9629 - val_accuracy: 0.5572 - val_loss: 1.0739 - learning_rate: 0.0010
Epoch 19/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 169ms/step - accuracy: 0.6250 - loss: 0.8319

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6178 - loss: 0.9040   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6025 - loss: 0.9162

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5960 - loss: 0.9319

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5919 - loss: 0.9398

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5905 - loss: 0.9414

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5907 - loss: 0.9416

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5912 - loss: 0.9411

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5917 - loss: 0.9403

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5922 - loss: 0.9396

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5927 - loss: 0.9393

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5931 - loss: 0.9401

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5929 - loss: 0.9414

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5926 - loss: 0.9430

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5924 - loss: 0.9445

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5921 - loss: 0.9457

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5918 - loss: 0.9467

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5916 - loss: 0.9475

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5912 - loss: 0.9485

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5907 - loss: 0.9495

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5902 - loss: 0.9504

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5897 - loss: 0.9512

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5894 - loss: 0.9518

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5891 - loss: 0.9523

 73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5889 - loss: 0.9528

 76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5886 - loss: 0.9533

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5882 - loss: 0.9539

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5878 - loss: 0.9545

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5876 - loss: 0.9550

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5874 - loss: 0.9553

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9556

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9560

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9562

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9565

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9567

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9568

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9570

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9572

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9573

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5873 - loss: 0.9575

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5874 - loss: 0.9577

124/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5875 - loss: 0.9578

127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5877 - loss: 0.9579

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5877 - loss: 0.9579

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5879 - loss: 0.9580

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5880 - loss: 0.9581

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5881 - loss: 0.9582

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5881 - loss: 0.9583

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9584

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9585

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9586

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9587

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9588

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9589

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9589

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9591

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9591

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5882 - loss: 0.9593

174/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5882 - loss: 0.9594

177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5882 - loss: 0.9595

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9597

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9599

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5881 - loss: 0.9600

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9602

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9603

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9604

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5880 - loss: 0.9605

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9606

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9607

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9608

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9608

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9609

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9610

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9610

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5879 - loss: 0.9611

225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9612

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9612

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9613

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9613

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5879 - loss: 0.9614

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5880 - loss: 0.9615

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5881 - loss: 0.9615

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5881 - loss: 0.9615

273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5881 - loss: 0.9615

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5882 - loss: 0.9615

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9615

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9615

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9616

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5883 - loss: 0.9617

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9617

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5884 - loss: 0.9618

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9618

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9619

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9620

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9621

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5885 - loss: 0.9622

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5885 - loss: 0.9622

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9623

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9624

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9625

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9626

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9627

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5886 - loss: 0.9627

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9627

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9627

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9628

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9629

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5886 - loss: 0.9630

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9630

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9630

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5887 - loss: 0.9631
Epoch 19: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5887 - loss: 0.9632 - val_accuracy: 0.5433 - val_loss: 1.1012 - learning_rate: 0.0010
Epoch 20/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.7500 - loss: 0.7466

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.7031 - loss: 0.8127   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6855 - loss: 0.8420

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6754 - loss: 0.8626

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6679 - loss: 0.8791

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6633 - loss: 0.8895

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6587 - loss: 0.8995

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6531 - loss: 0.9093

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6480 - loss: 0.9158

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6450 - loss: 0.9192

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6420 - loss: 0.9227

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6384 - loss: 0.9267

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6356 - loss: 0.9294

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6326 - loss: 0.9324

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6295 - loss: 0.9358

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404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9638

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9637

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9637

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9637

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9636

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9636

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9635

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5972 - loss: 0.9635

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5972 - loss: 0.9635

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5972 - loss: 0.9635

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5972 - loss: 0.9634

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5972 - loss: 0.9634

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9634

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9634

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5971 - loss: 0.9633

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5970 - loss: 0.9633

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5970 - loss: 0.9632
Epoch 20: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5970 - loss: 0.9632 - val_accuracy: 0.5045 - val_loss: 1.1262 - learning_rate: 0.0010
Epoch 21/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5625 - loss: 0.8969

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5788 - loss: 0.9398   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5934 - loss: 0.9419

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6007 - loss: 0.9401

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6114 - loss: 0.9353

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6181 - loss: 0.9283

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6240 - loss: 0.9207

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6273 - loss: 0.9156 

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6286 - loss: 0.9149

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6281 - loss: 0.9164

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6278 - loss: 0.9179

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6269 - loss: 0.9200

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6262 - loss: 0.9216

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6250 - loss: 0.9242

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6240 - loss: 0.9260

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6231 - loss: 0.9278

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6225 - loss: 0.9290

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6219 - loss: 0.9302

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6209 - loss: 0.9317

 54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6200 - loss: 0.9331

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6190 - loss: 0.9345

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6181 - loss: 0.9359

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6173 - loss: 0.9374

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6165 - loss: 0.9386

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6160 - loss: 0.9397

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6155 - loss: 0.9406

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6152 - loss: 0.9412

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6149 - loss: 0.9416

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6145 - loss: 0.9421

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6141 - loss: 0.9428

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6136 - loss: 0.9434

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6132 - loss: 0.9439

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6129 - loss: 0.9443

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6126 - loss: 0.9448

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6123 - loss: 0.9452

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6120 - loss: 0.9456

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6118 - loss: 0.9458

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6115 - loss: 0.9462

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6113 - loss: 0.9466

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6110 - loss: 0.9469

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6109 - loss: 0.9471

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6107 - loss: 0.9473

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6105 - loss: 0.9475

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6104 - loss: 0.9477

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6102 - loss: 0.9479

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6101 - loss: 0.9480

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6100 - loss: 0.9482

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6098 - loss: 0.9484

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6096 - loss: 0.9486

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6095 - loss: 0.9488

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6093 - loss: 0.9491

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6091 - loss: 0.9494

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6089 - loss: 0.9497

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6088 - loss: 0.9500

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6085 - loss: 0.9503

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6083 - loss: 0.9507

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6081 - loss: 0.9510

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6078 - loss: 0.9514

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6076 - loss: 0.9517

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6075 - loss: 0.9519

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6073 - loss: 0.9522

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6071 - loss: 0.9526

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6068 - loss: 0.9529

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6066 - loss: 0.9533

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6064 - loss: 0.9536

187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6062 - loss: 0.9538

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6059 - loss: 0.9542

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6057 - loss: 0.9545

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6054 - loss: 0.9549

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6052 - loss: 0.9552

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6050 - loss: 0.9555

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6047 - loss: 0.9558

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6045 - loss: 0.9561

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6043 - loss: 0.9563

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6041 - loss: 0.9566

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6039 - loss: 0.9568

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6037 - loss: 0.9571

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6036 - loss: 0.9574

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6034 - loss: 0.9576

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6032 - loss: 0.9578

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6030 - loss: 0.9581

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6029 - loss: 0.9583

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6027 - loss: 0.9585

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6025 - loss: 0.9587

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6024 - loss: 0.9589

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6022 - loss: 0.9591

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6021 - loss: 0.9593

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6020 - loss: 0.9594

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6019 - loss: 0.9596

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6017 - loss: 0.9598

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6016 - loss: 0.9599

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6015 - loss: 0.9601

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6014 - loss: 0.9602

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6012 - loss: 0.9604

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6011 - loss: 0.9605

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6010 - loss: 0.9607

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6009 - loss: 0.9608

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6009 - loss: 0.9609

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6008 - loss: 0.9610

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6007 - loss: 0.9611

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6007 - loss: 0.9611

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6006 - loss: 0.9612

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6005 - loss: 0.9613

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6005 - loss: 0.9613

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6004 - loss: 0.9614

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6004 - loss: 0.9615

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6003 - loss: 0.9616

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6002 - loss: 0.9616

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6002 - loss: 0.9617

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6001 - loss: 0.9618

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6000 - loss: 0.9619

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6000 - loss: 0.9619

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5999 - loss: 0.9620

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5999 - loss: 0.9620

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5998 - loss: 0.9621

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5997 - loss: 0.9621

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5997 - loss: 0.9622

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5996 - loss: 0.9622

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5995 - loss: 0.9623

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5995 - loss: 0.9623

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5994 - loss: 0.9623

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5994 - loss: 0.9623

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5993 - loss: 0.9623

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5993 - loss: 0.9624

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5992 - loss: 0.9624

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5992 - loss: 0.9624

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5991 - loss: 0.9624

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5991 - loss: 0.9624

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5990 - loss: 0.9624

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5990 - loss: 0.9625

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5989 - loss: 0.9625

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5989 - loss: 0.9625

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5988 - loss: 0.9625

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5988 - loss: 0.9626

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5987 - loss: 0.9626

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5987 - loss: 0.9626

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9626

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9627

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5986 - loss: 0.9627

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5985 - loss: 0.9627

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5985 - loss: 0.9627

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5984 - loss: 0.9628

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5984 - loss: 0.9628

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5983 - loss: 0.9628

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5982 - loss: 0.9629

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5982 - loss: 0.9629

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5981 - loss: 0.9629

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5981 - loss: 0.9630

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5980 - loss: 0.9630

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5980 - loss: 0.9630

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5979 - loss: 0.9630

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5978 - loss: 0.9631

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5978 - loss: 0.9631

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5977 - loss: 0.9631

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5977 - loss: 0.9631

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5976 - loss: 0.9631

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5975 - loss: 0.9631

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5975 - loss: 0.9631
Epoch 21: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 21: val_accuracy did not improve from 0.61141

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5974 - loss: 0.9632 - val_accuracy: 0.5821 - val_loss: 0.9879 - learning_rate: 0.0010
Epoch 22/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 176ms/step - accuracy: 0.5312 - loss: 1.0048

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5814 - loss: 0.9163   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5817 - loss: 0.9304

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5778 - loss: 0.9447

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5812 - loss: 0.9461

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5837 - loss: 0.9450

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5863 - loss: 0.9423

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5891 - loss: 0.9395 

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5916 - loss: 0.9370

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5940 - loss: 0.9343

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5964 - loss: 0.9314

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5977 - loss: 0.9298

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5991 - loss: 0.9281

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5999 - loss: 0.9271

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6012 - loss: 0.9255

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6019 - loss: 0.9245

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6026 - loss: 0.9236

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6031 - loss: 0.9227

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6036 - loss: 0.9220

 54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6039 - loss: 0.9215

 56/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6042 - loss: 0.9211

 58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6045 - loss: 0.9208

 60/473 ━━━━━━━━━━━━━━━━━━━━ 9s 24ms/step - accuracy: 0.6048 - loss: 0.9204

 63/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6052 - loss: 0.9201

 66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6055 - loss: 0.9198

 69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6057 - loss: 0.9198

 72/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6058 - loss: 0.9199

 75/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.6059 - loss: 0.9201

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9204

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6058 - loss: 0.9207

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6059 - loss: 0.9209

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9211

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6058 - loss: 0.9213

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9214

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 23ms/step - accuracy: 0.6059 - loss: 0.9215

 97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6060 - loss: 0.9216

100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6060 - loss: 0.9217

103/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9217

105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9218

108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9219

111/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6061 - loss: 0.9220

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9221

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9222

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6061 - loss: 0.9222

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9222

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9222

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6062 - loss: 0.9221

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6063 - loss: 0.9221

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6064 - loss: 0.9220

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6065 - loss: 0.9219

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 23ms/step - accuracy: 0.6066 - loss: 0.9219

139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6067 - loss: 0.9218

142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6068 - loss: 0.9218

144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6069 - loss: 0.9217

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6070 - loss: 0.9217

149/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6071 - loss: 0.9217

152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6071 - loss: 0.9217

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Epoch 22: val_accuracy improved from 0.61141 to 0.63593, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6079 - loss: 0.9255 - val_accuracy: 0.6359 - val_loss: 0.8763 - learning_rate: 2.0000e-04
Epoch 23/40
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391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6160 - loss: 0.9221

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6160 - loss: 0.9221

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6159 - loss: 0.9222

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6159 - loss: 0.9222

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6159 - loss: 0.9222

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6159 - loss: 0.9223

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9223

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9223

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9223

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9224

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9224

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6158 - loss: 0.9224

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9225

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9226

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6157 - loss: 0.9226

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9226

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6156 - loss: 0.9227

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6155 - loss: 0.9228

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6154 - loss: 0.9229
Epoch 23: val_accuracy did not improve from 0.63593

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6154 - loss: 0.9229 - val_accuracy: 0.6062 - val_loss: 0.9353 - learning_rate: 2.0000e-04
Epoch 24/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:19 168ms/step - accuracy: 0.5938 - loss: 0.8949

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6230 - loss: 0.8562   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6289 - loss: 0.8521

  9/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6285 - loss: 0.8580

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6281 - loss: 0.8647

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6305 - loss: 0.8660

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6309 - loss: 0.8683

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6302 - loss: 0.8720

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6288 - loss: 0.8756

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6276 - loss: 0.8789

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6263 - loss: 0.8823

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6246 - loss: 0.8862

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6230 - loss: 0.8898

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6212 - loss: 0.8939

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6205 - loss: 0.8965

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6200 - loss: 0.8984

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6195 - loss: 0.8995

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6191 - loss: 0.9006

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6185 - loss: 0.9024

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6179 - loss: 0.9039

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6174 - loss: 0.9053

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6170 - loss: 0.9063

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6165 - loss: 0.9075

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6159 - loss: 0.9087

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6156 - loss: 0.9094

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6151 - loss: 0.9104

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6149 - loss: 0.9111

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6146 - loss: 0.9116

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6142 - loss: 0.9124

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.9130

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6135 - loss: 0.9137

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6131 - loss: 0.9144

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6129 - loss: 0.9148

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6126 - loss: 0.9153

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6123 - loss: 0.9161

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6121 - loss: 0.9166

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6118 - loss: 0.9174

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6114 - loss: 0.9184

104/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9189

107/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6108 - loss: 0.9198

110/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6105 - loss: 0.9207

112/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6103 - loss: 0.9212

114/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6101 - loss: 0.9216

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6099 - loss: 0.9223

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6096 - loss: 0.9228

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6095 - loss: 0.9233

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6093 - loss: 0.9238

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6091 - loss: 0.9243

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6089 - loss: 0.9247

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6087 - loss: 0.9252

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6086 - loss: 0.9255

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6085 - loss: 0.9259

144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6084 - loss: 0.9262

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6084 - loss: 0.9264

150/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6083 - loss: 0.9267

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9269

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9270

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6082 - loss: 0.9272

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6081 - loss: 0.9274

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6081 - loss: 0.9276

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6080 - loss: 0.9278

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6080 - loss: 0.9280

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9281

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9282

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9284

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6079 - loss: 0.9285

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9287

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9289

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9291

190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6078 - loss: 0.9292

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6077 - loss: 0.9294

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9295

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9297

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9298

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9298

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9299

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9300

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6077 - loss: 0.9301

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6078 - loss: 0.9302

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6079 - loss: 0.9302

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6079 - loss: 0.9302

234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302

237/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302

240/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6080 - loss: 0.9302

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6080 - loss: 0.9302

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6081 - loss: 0.9302

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Epoch 24: val_accuracy improved from 0.63593 to 0.63653, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

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Epoch 25/40
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153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.6199 - loss: 0.9157

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6198 - loss: 0.9158

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6198 - loss: 0.9159

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6197 - loss: 0.9160

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6196 - loss: 0.9161

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6196 - loss: 0.9162

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6195 - loss: 0.9164

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6194 - loss: 0.9165

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.6192 - loss: 0.9167

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.6191 - loss: 0.9168

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6190 - loss: 0.9170

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6189 - loss: 0.9171

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6188 - loss: 0.9172

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6187 - loss: 0.9174

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6186 - loss: 0.9175

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6185 - loss: 0.9176

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6184 - loss: 0.9178

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6183 - loss: 0.9179

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6182 - loss: 0.9180

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6182 - loss: 0.9181

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6181 - loss: 0.9182

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6181 - loss: 0.9183

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6180 - loss: 0.9184

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6179 - loss: 0.9184

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.6179 - loss: 0.9185

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6178 - loss: 0.9185

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6178 - loss: 0.9185

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6177 - loss: 0.9186

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6177 - loss: 0.9186

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9186

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9187

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6176 - loss: 0.9187

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6175 - loss: 0.9188

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6175 - loss: 0.9188

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9189

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9189

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6174 - loss: 0.9190

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9190

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9190

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9191

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.6173 - loss: 0.9191

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9191

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6173 - loss: 0.9192

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6172 - loss: 0.9192

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.6172 - loss: 0.9192

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9192

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9192

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9193

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9193

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9194

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6172 - loss: 0.9194

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9195

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9195

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6171 - loss: 0.9196

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9196

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9196

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9196

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6171 - loss: 0.9197

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6172 - loss: 0.9197

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9196

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9195

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6171 - loss: 0.9195

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6171 - loss: 0.9196

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9196

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6170 - loss: 0.9197

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6169 - loss: 0.9197
Epoch 25: val_accuracy did not improve from 0.63653

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6169 - loss: 0.9197 - val_accuracy: 0.6359 - val_loss: 0.8749 - learning_rate: 2.0000e-04
Epoch 26/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 170ms/step - accuracy: 0.6562 - loss: 0.7736

  3/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.6858 - loss: 0.7739  

  5/473 ━━━━━━━━━━━━━━━━━━━━ 13s 30ms/step - accuracy: 0.6808 - loss: 0.8023

  8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6619 - loss: 0.8551

 11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6501 - loss: 0.8793

 14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6442 - loss: 0.8926

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6401 - loss: 0.9011

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6367 - loss: 0.9068 

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382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9176

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9176

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9176

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6143 - loss: 0.9177

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9177

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9177

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9177

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9177

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9178

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9178

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9178

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9179

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9179

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6144 - loss: 0.9179

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6145 - loss: 0.9179

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9179

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9180

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9181

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6145 - loss: 0.9181

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9181

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6146 - loss: 0.9182
Epoch 26: val_accuracy did not improve from 0.63653

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6146 - loss: 0.9183 - val_accuracy: 0.6241 - val_loss: 0.9116 - learning_rate: 2.0000e-04
Epoch 27/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 174ms/step - accuracy: 0.6250 - loss: 0.9067

  3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 32ms/step - accuracy: 0.6111 - loss: 0.8998  

  5/473 ━━━━━━━━━━━━━━━━━━━━ 13s 28ms/step - accuracy: 0.5985 - loss: 0.9138

  8/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5945 - loss: 0.9277

 11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5870 - loss: 0.9407

 14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5861 - loss: 0.9457

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5876 - loss: 0.9462

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5902 - loss: 0.9433 

 23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5938 - loss: 0.9391

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5957 - loss: 0.9368

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5964 - loss: 0.9365

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5961 - loss: 0.9379

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5959 - loss: 0.9387

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5963 - loss: 0.9385

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5964 - loss: 0.9387

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5965 - loss: 0.9390

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5961 - loss: 0.9401

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9410

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5953 - loss: 0.9419

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5951 - loss: 0.9424

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5949 - loss: 0.9427

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5948 - loss: 0.9430

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9431

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9430

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9430

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5946 - loss: 0.9432

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9432

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5947 - loss: 0.9432

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5948 - loss: 0.9432

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5950 - loss: 0.9431

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5951 - loss: 0.9431

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5953 - loss: 0.9429

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5955 - loss: 0.9428

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5956 - loss: 0.9427

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5959 - loss: 0.9424

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5960 - loss: 0.9422

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5962 - loss: 0.9419

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5964 - loss: 0.9417

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5966 - loss: 0.9415

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5968 - loss: 0.9413

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5970 - loss: 0.9411

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5971 - loss: 0.9410

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5973 - loss: 0.9409

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5975 - loss: 0.9408

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5976 - loss: 0.9407

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5978 - loss: 0.9405

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5980 - loss: 0.9404

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5982 - loss: 0.9402

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5985 - loss: 0.9400

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5987 - loss: 0.9398

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5988 - loss: 0.9396

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5990 - loss: 0.9394

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5991 - loss: 0.9393

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5993 - loss: 0.9392

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5994 - loss: 0.9390

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5995 - loss: 0.9389

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5996 - loss: 0.9387

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5997 - loss: 0.9385

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5999 - loss: 0.9384

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6000 - loss: 0.9382

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6001 - loss: 0.9380

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6003 - loss: 0.9378

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6004 - loss: 0.9377

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6006 - loss: 0.9374

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6008 - loss: 0.9372

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6010 - loss: 0.9370

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6011 - loss: 0.9368

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6013 - loss: 0.9366

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6014 - loss: 0.9364

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6015 - loss: 0.9363

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6017 - loss: 0.9361

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6018 - loss: 0.9360

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6019 - loss: 0.9359

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6020 - loss: 0.9357

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6021 - loss: 0.9355

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6022 - loss: 0.9354

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6023 - loss: 0.9352

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6024 - loss: 0.9350

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6025 - loss: 0.9348

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6026 - loss: 0.9346

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6028 - loss: 0.9344

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6029 - loss: 0.9343

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6030 - loss: 0.9341

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6031 - loss: 0.9339

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6032 - loss: 0.9337

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6033 - loss: 0.9335

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6035 - loss: 0.9333

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6036 - loss: 0.9332

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6037 - loss: 0.9329

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6038 - loss: 0.9327

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6039 - loss: 0.9325

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Epoch 27: val_accuracy improved from 0.63653 to 0.65240, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_vgg16_20240411-001411.keras

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Epoch 28/40
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142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6250 - loss: 0.8978

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6250 - loss: 0.8980

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6251 - loss: 0.8981

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6251 - loss: 0.8981

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6251 - loss: 0.8983

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6251 - loss: 0.8984

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6250 - loss: 0.8986

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6250 - loss: 0.8987

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6250 - loss: 0.8989

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6249 - loss: 0.8991

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6249 - loss: 0.8993

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6248 - loss: 0.8995

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6248 - loss: 0.8997

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6248 - loss: 0.8999

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9001

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9002

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6247 - loss: 0.9003

189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6246 - loss: 0.9005

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6246 - loss: 0.9007

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9008

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9009

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6245 - loss: 0.9010

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6244 - loss: 0.9011

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6244 - loss: 0.9013

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6244 - loss: 0.9015

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6243 - loss: 0.9016

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6243 - loss: 0.9018

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9019

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9020

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9021

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9022

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9023

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9024

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9025

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6242 - loss: 0.9026

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9027

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9028

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9029

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6241 - loss: 0.9030

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9031

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9031

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9032

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6240 - loss: 0.9034

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6239 - loss: 0.9035

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6239 - loss: 0.9036

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6238 - loss: 0.9037

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6238 - loss: 0.9038

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9040

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9040

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6237 - loss: 0.9041

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6236 - loss: 0.9042

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6236 - loss: 0.9043

283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6236 - loss: 0.9044

286/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6235 - loss: 0.9045

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6234 - loss: 0.9046

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6234 - loss: 0.9047

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6233 - loss: 0.9048

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6233 - loss: 0.9048

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6233 - loss: 0.9049

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6232 - loss: 0.9050

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6232 - loss: 0.9051

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9052

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9052

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6231 - loss: 0.9053

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6230 - loss: 0.9053

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6230 - loss: 0.9053

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6230 - loss: 0.9054

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6229 - loss: 0.9055

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6229 - loss: 0.9055

330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6228 - loss: 0.9056

333/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6228 - loss: 0.9057

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6227 - loss: 0.9057

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6227 - loss: 0.9058

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6226 - loss: 0.9059

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6226 - loss: 0.9060

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6225 - loss: 0.9060

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6224 - loss: 0.9061

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6224 - loss: 0.9062

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9062

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9063

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6223 - loss: 0.9064

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9065

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9066

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9066

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9067

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9068

379/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9069

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6219 - loss: 0.9069

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6219 - loss: 0.9070

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6218 - loss: 0.9071

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6218 - loss: 0.9072

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6217 - loss: 0.9072

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6216 - loss: 0.9073

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6216 - loss: 0.9074

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6216 - loss: 0.9074

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6215 - loss: 0.9075

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6214 - loss: 0.9076

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6214 - loss: 0.9077

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6213 - loss: 0.9078

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6213 - loss: 0.9078

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6212 - loss: 0.9079

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6212 - loss: 0.9080

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6211 - loss: 0.9080

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6211 - loss: 0.9081

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6210 - loss: 0.9081

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6210 - loss: 0.9082

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6209 - loss: 0.9082

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6209 - loss: 0.9083

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6208 - loss: 0.9084

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6208 - loss: 0.9085

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9085

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9086

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6207 - loss: 0.9086

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9087

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9087

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6206 - loss: 0.9088

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6205 - loss: 0.9089
Epoch 28: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6205 - loss: 0.9089 - val_accuracy: 0.5927 - val_loss: 0.9807 - learning_rate: 2.0000e-04
Epoch 29/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.5938 - loss: 1.0521

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5514 - loss: 1.0918   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5661 - loss: 1.0529

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365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6120 - loss: 0.9262

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6120 - loss: 0.9261

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6120 - loss: 0.9261

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6121 - loss: 0.9260

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6121 - loss: 0.9260

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6121 - loss: 0.9259

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6121 - loss: 0.9259

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6121 - loss: 0.9258

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6122 - loss: 0.9257

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6122 - loss: 0.9257

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6122 - loss: 0.9256

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6122 - loss: 0.9256

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9255

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9254

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9254

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6123 - loss: 0.9253

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9253

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9252

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6124 - loss: 0.9252

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9251

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9251

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6125 - loss: 0.9250

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9250

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9250

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6125 - loss: 0.9249

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9249

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9249

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9248

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9248

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9247

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6126 - loss: 0.9247

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9247

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9246

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6127 - loss: 0.9245
Epoch 29: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6127 - loss: 0.9244 - val_accuracy: 0.6303 - val_loss: 0.8999 - learning_rate: 2.0000e-04
Epoch 30/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:24 180ms/step - accuracy: 0.5625 - loss: 0.9556

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6165 - loss: 0.8338   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6268 - loss: 0.8316

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6200 - loss: 0.8398

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6150 - loss: 0.8494

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6148 - loss: 0.8545

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6149 - loss: 0.8571

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6135 - loss: 0.8619

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6134 - loss: 0.8655

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6130 - loss: 0.8679

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6123 - loss: 0.8736

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6121 - loss: 0.8762

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6118 - loss: 0.8800

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6115 - loss: 0.8829

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6116 - loss: 0.8852

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.8865

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6117 - loss: 0.8883

 50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6119 - loss: 0.8898

 53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6121 - loss: 0.8911

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6123 - loss: 0.8918

 58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6125 - loss: 0.8927

 61/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6127 - loss: 0.8938

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6128 - loss: 0.8947

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6129 - loss: 0.8956

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6131 - loss: 0.8964

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6133 - loss: 0.8970

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6135 - loss: 0.8974

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6136 - loss: 0.8978

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6137 - loss: 0.8981

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.8986

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6139 - loss: 0.8992

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6140 - loss: 0.8998

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6142 - loss: 0.9002

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6143 - loss: 0.9004

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6144 - loss: 0.9008

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6145 - loss: 0.9011

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6146 - loss: 0.9014

105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6147 - loss: 0.9017

108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6149 - loss: 0.9020

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6150 - loss: 0.9022

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6151 - loss: 0.9025

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6152 - loss: 0.9027

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6154 - loss: 0.9030

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6155 - loss: 0.9032

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6156 - loss: 0.9034

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6157 - loss: 0.9035

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6159 - loss: 0.9037

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6160 - loss: 0.9038

137/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6161 - loss: 0.9039

140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9041

142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9043

144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9045

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9047

150/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9049

152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9050

155/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6165 - loss: 0.9052

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6165 - loss: 0.9054

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6166 - loss: 0.9056

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6166 - loss: 0.9058

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9060

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9061

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9062

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9064

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9066

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9069

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9072

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9075

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9076

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9078

191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9080

194/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6168 - loss: 0.9082

197/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6169 - loss: 0.9084

200/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6170 - loss: 0.9085

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6170 - loss: 0.9087

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6171 - loss: 0.9088

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6172 - loss: 0.9089

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6172 - loss: 0.9090

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6173 - loss: 0.9091

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6174 - loss: 0.9092

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6174 - loss: 0.9092

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6175 - loss: 0.9093

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6176 - loss: 0.9094

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6176 - loss: 0.9094

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6177 - loss: 0.9095

235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6177 - loss: 0.9096

238/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6178 - loss: 0.9097

241/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6178 - loss: 0.9098

244/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6179 - loss: 0.9099

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6179 - loss: 0.9099

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6179 - loss: 0.9100

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9101

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9102

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6180 - loss: 0.9102

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9103

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9104

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9105

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9105

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9106

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9107

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9107

282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9108

285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6181 - loss: 0.9109

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9109

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9110

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9110

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9111

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9111

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6181 - loss: 0.9112

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9113

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9113

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9114

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9114

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9115

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9115

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9116

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9116

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9117

330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9117

333/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6180 - loss: 0.9118

335/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6179 - loss: 0.9118

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9119

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9119

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9120

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9120

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9121

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9121

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9122

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6179 - loss: 0.9123

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9123

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9123

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9124

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9124

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9125

378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9125

381/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6178 - loss: 0.9126

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6178 - loss: 0.9126

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6178 - loss: 0.9126

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9127

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9127

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9128

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9128

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9129

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9130

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6177 - loss: 0.9130

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9130

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9131

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9132

426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6176 - loss: 0.9132

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9132

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9132

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9133

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6176 - loss: 0.9134

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9134

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9135

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9135

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9136

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9136

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9137

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6175 - loss: 0.9137

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6174 - loss: 0.9138

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6174 - loss: 0.9139
Epoch 30: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6174 - loss: 0.9140 - val_accuracy: 0.6184 - val_loss: 0.9022 - learning_rate: 2.0000e-04
Epoch 31/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:23 176ms/step - accuracy: 0.5938 - loss: 1.0068

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6094 - loss: 0.9844   

  6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6043 - loss: 0.9937

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.6016 - loss: 0.9900

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6009 - loss: 0.9837

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6018 - loss: 0.9742

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6033 - loss: 0.9686

 20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6049 - loss: 0.9613

 23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6062 - loss: 0.9558 

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6073 - loss: 0.9509

 28/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6079 - loss: 0.9476

 31/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6088 - loss: 0.9427

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9388 

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9359

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6094 - loss: 0.9334

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6095 - loss: 0.9314

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6095 - loss: 0.9295

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6096 - loss: 0.9278

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6096 - loss: 0.9264

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6097 - loss: 0.9250

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6099 - loss: 0.9237

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6102 - loss: 0.9223

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6104 - loss: 0.9214

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6106 - loss: 0.9206

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6106 - loss: 0.9200

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6107 - loss: 0.9193

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6107 - loss: 0.9191

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6109 - loss: 0.9186

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6110 - loss: 0.9183

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6110 - loss: 0.9182

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6111 - loss: 0.9180

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6111 - loss: 0.9178

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9176

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9175

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6112 - loss: 0.9174

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457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6149 - loss: 0.9168

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6150 - loss: 0.9168
Epoch 31: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6151 - loss: 0.9168 - val_accuracy: 0.6245 - val_loss: 0.9051 - learning_rate: 2.0000e-04
Epoch 32/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.5625 - loss: 0.9757

  3/473 ━━━━━━━━━━━━━━━━━━━━ 14s 31ms/step - accuracy: 0.5677 - loss: 0.9618  

  5/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5831 - loss: 0.9571

  8/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5899 - loss: 0.9525

 11/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5915 - loss: 0.9515

 13/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5911 - loss: 0.9519

 16/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5921 - loss: 0.9481

 19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5919 - loss: 0.9459

 21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5907 - loss: 0.9456

 23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5903 - loss: 0.9443

 26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5901 - loss: 0.9429

 29/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5904 - loss: 0.9409

 32/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5909 - loss: 0.9396

 35/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5918 - loss: 0.9376

 37/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5922 - loss: 0.9366

 39/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5926 - loss: 0.9358

 42/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5933 - loss: 0.9350

 45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5942 - loss: 0.9340 

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5949 - loss: 0.9335

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5957 - loss: 0.9327

 54/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5965 - loss: 0.9317

 57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5972 - loss: 0.9310

 60/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5978 - loss: 0.9303

 63/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5986 - loss: 0.9293

 66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5994 - loss: 0.9282

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6002 - loss: 0.9273

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6010 - loss: 0.9264

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6018 - loss: 0.9255

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6025 - loss: 0.9247

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6029 - loss: 0.9243

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6034 - loss: 0.9237

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6038 - loss: 0.9235

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6041 - loss: 0.9234

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6044 - loss: 0.9232

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6048 - loss: 0.9230

 97/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6053 - loss: 0.9227

100/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6057 - loss: 0.9225

102/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6059 - loss: 0.9223

105/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6063 - loss: 0.9220

108/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6066 - loss: 0.9217

110/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.6068 - loss: 0.9216

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6070 - loss: 0.9215

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6072 - loss: 0.9213

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6074 - loss: 0.9212

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6076 - loss: 0.9210

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6078 - loss: 0.9209

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6080 - loss: 0.9208

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6082 - loss: 0.9207

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6083 - loss: 0.9206

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6085 - loss: 0.9205

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6086 - loss: 0.9205

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6088 - loss: 0.9204

144/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6089 - loss: 0.9203

147/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6090 - loss: 0.9202

150/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6092 - loss: 0.9202

152/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6093 - loss: 0.9201

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6094 - loss: 0.9201

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6095 - loss: 0.9200

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6097 - loss: 0.9199

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6098 - loss: 0.9198

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6099 - loss: 0.9197

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6101 - loss: 0.9196

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6102 - loss: 0.9196

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6103 - loss: 0.9195

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6104 - loss: 0.9194

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6106 - loss: 0.9194

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6107 - loss: 0.9193

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6108 - loss: 0.9193

188/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6109 - loss: 0.9192

191/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6110 - loss: 0.9191

194/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6112 - loss: 0.9191

197/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6113 - loss: 0.9191

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6114 - loss: 0.9191

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6115 - loss: 0.9190

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6116 - loss: 0.9190

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6117 - loss: 0.9190

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6118 - loss: 0.9190

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6119 - loss: 0.9190

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6121 - loss: 0.9190

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6122 - loss: 0.9189

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6123 - loss: 0.9189

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6124 - loss: 0.9189

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6125 - loss: 0.9189

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6126 - loss: 0.9189

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6127 - loss: 0.9189

235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6129 - loss: 0.9189

238/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6130 - loss: 0.9188

241/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6132 - loss: 0.9188

244/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.6133 - loss: 0.9187

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6134 - loss: 0.9187

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6135 - loss: 0.9186

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6137 - loss: 0.9186

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6138 - loss: 0.9185

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6140 - loss: 0.9184

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6140 - loss: 0.9184

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6141 - loss: 0.9183

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6142 - loss: 0.9182

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6144 - loss: 0.9181

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6145 - loss: 0.9181

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6146 - loss: 0.9180

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6147 - loss: 0.9180

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6148 - loss: 0.9179

283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6149 - loss: 0.9179

286/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6150 - loss: 0.9179

289/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.6151 - loss: 0.9178

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6152 - loss: 0.9178

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6153 - loss: 0.9177

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6154 - loss: 0.9177

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6154 - loss: 0.9177

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6155 - loss: 0.9176

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6156 - loss: 0.9176

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6156 - loss: 0.9176

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6157 - loss: 0.9176

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6157 - loss: 0.9175

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6158 - loss: 0.9175

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6159 - loss: 0.9175

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6159 - loss: 0.9175

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6160 - loss: 0.9175

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6160 - loss: 0.9175

331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6161 - loss: 0.9174

334/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.6161 - loss: 0.9174

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6162 - loss: 0.9174

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6162 - loss: 0.9174

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6163 - loss: 0.9175

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6163 - loss: 0.9175

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6164 - loss: 0.9175

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6164 - loss: 0.9175

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6164 - loss: 0.9175

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6165 - loss: 0.9175

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6165 - loss: 0.9175

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6166 - loss: 0.9174

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6166 - loss: 0.9174

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6167 - loss: 0.9174

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6167 - loss: 0.9174

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6168 - loss: 0.9174

377/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6168 - loss: 0.9174

380/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.6169 - loss: 0.9173

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6169 - loss: 0.9173

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6170 - loss: 0.9173

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6171 - loss: 0.9173

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6172 - loss: 0.9173

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9173

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6172 - loss: 0.9173

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173

426/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6173 - loss: 0.9173

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6173 - loss: 0.9173

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6173 - loss: 0.9173

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6174 - loss: 0.9173

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9173

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9173

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9174

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6175 - loss: 0.9174
Epoch 32: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 32: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6175 - loss: 0.9174 - val_accuracy: 0.5919 - val_loss: 1.0019 - learning_rate: 2.0000e-04
Epoch 33/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:39 211ms/step - accuracy: 0.6562 - loss: 0.8991

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6868 - loss: 0.8418   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6874 - loss: 0.8426

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6788 - loss: 0.8512

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6713 - loss: 0.8569

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6618 - loss: 0.8664

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6546 - loss: 0.8745

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6501 - loss: 0.8797

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6474 - loss: 0.8830

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6451 - loss: 0.8857

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6424 - loss: 0.8884

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6400 - loss: 0.8912

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6381 - loss: 0.8931

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6362 - loss: 0.8952

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6345 - loss: 0.8976

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6328 - loss: 0.8999

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6311 - loss: 0.9021

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6294 - loss: 0.9042

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6283 - loss: 0.9055

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6269 - loss: 0.9074

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6256 - loss: 0.9094

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6245 - loss: 0.9110

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6236 - loss: 0.9127

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6225 - loss: 0.9144

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6215 - loss: 0.9159

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6206 - loss: 0.9171

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6199 - loss: 0.9182

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6192 - loss: 0.9192

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6187 - loss: 0.9202

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6184 - loss: 0.9207

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6181 - loss: 0.9214

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6177 - loss: 0.9221

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6174 - loss: 0.9226

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6172 - loss: 0.9231

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6170 - loss: 0.9236

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6168 - loss: 0.9240

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6166 - loss: 0.9243

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6165 - loss: 0.9246

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6164 - loss: 0.9247

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6163 - loss: 0.9248

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6162 - loss: 0.9248

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6162 - loss: 0.9249

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9249

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6160 - loss: 0.9249

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6160 - loss: 0.9248

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9247

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.6161 - loss: 0.9246

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6161 - loss: 0.9245

140/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9244

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6162 - loss: 0.9242

146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6163 - loss: 0.9240

148/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6164 - loss: 0.9239

151/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6165 - loss: 0.9237

153/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.6166 - loss: 0.9236

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6167 - loss: 0.9234

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6169 - loss: 0.9231

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6170 - loss: 0.9229

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6171 - loss: 0.9227

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6172 - loss: 0.9225

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6173 - loss: 0.9223

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6174 - loss: 0.9221

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6175 - loss: 0.9220

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6176 - loss: 0.9217

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6178 - loss: 0.9216

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.6179 - loss: 0.9214

189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6180 - loss: 0.9212

192/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.6181 - loss: 0.9211

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6182 - loss: 0.9209

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6183 - loss: 0.9207

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6184 - loss: 0.9205

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6185 - loss: 0.9204

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6186 - loss: 0.9202

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6187 - loss: 0.9201

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6188 - loss: 0.9199

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6189 - loss: 0.9198

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6190 - loss: 0.9196

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6191 - loss: 0.9194

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6192 - loss: 0.9192

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6193 - loss: 0.9191

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6194 - loss: 0.9189

234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.6196 - loss: 0.9187

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6197 - loss: 0.9185

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6198 - loss: 0.9184

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6199 - loss: 0.9182

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6200 - loss: 0.9180

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6201 - loss: 0.9179

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6202 - loss: 0.9177

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6203 - loss: 0.9175

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6204 - loss: 0.9174

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6205 - loss: 0.9173

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6206 - loss: 0.9171

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6207 - loss: 0.9170

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6208 - loss: 0.9169

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6209 - loss: 0.9167

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6209 - loss: 0.9167

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6210 - loss: 0.9166

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.6211 - loss: 0.9165

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6212 - loss: 0.9164

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6212 - loss: 0.9163

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6213 - loss: 0.9162

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6213 - loss: 0.9161

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6214 - loss: 0.9161

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6214 - loss: 0.9160

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6215 - loss: 0.9160

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6215 - loss: 0.9160

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6216 - loss: 0.9159

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6217 - loss: 0.9159

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6218 - loss: 0.9158

331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.6219 - loss: 0.9158

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6219 - loss: 0.9158

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9158

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6220 - loss: 0.9157

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6221 - loss: 0.9157

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9157

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9156

378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.6222 - loss: 0.9156

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9156

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6223 - loss: 0.9155

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9155

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9154

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6224 - loss: 0.9154

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9154

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9154

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.6225 - loss: 0.9153

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9153

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9152

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6226 - loss: 0.9151

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9151

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9150

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9149

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6227 - loss: 0.9149

471/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6228 - loss: 0.9148
Epoch 33: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6228 - loss: 0.9148 - val_accuracy: 0.6432 - val_loss: 0.8618 - learning_rate: 4.0000e-05
Epoch 34/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.5625 - loss: 0.9791

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5957 - loss: 0.9582   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5933 - loss: 0.9645

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5945 - loss: 0.9612

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5947 - loss: 0.9603

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5971 - loss: 0.9575

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6005 - loss: 0.9542

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6045 - loss: 0.9491

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6067 - loss: 0.9464

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6088 - loss: 0.9438

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6102 - loss: 0.9411

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6111 - loss: 0.9391

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6114 - loss: 0.9376

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6114 - loss: 0.9368

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6114 - loss: 0.9364

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6115 - loss: 0.9360

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6117 - loss: 0.9351

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6119 - loss: 0.9342

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6123 - loss: 0.9330

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6128 - loss: 0.9318

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6134 - loss: 0.9305

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6140 - loss: 0.9293

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6142 - loss: 0.9286

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6146 - loss: 0.9276

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6150 - loss: 0.9266

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6154 - loss: 0.9259

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6157 - loss: 0.9253

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436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6242 - loss: 0.9028

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6242 - loss: 0.9028

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6242 - loss: 0.9028

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9028

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9028

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9028

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9029

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9029

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9029

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9029

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6241 - loss: 0.9029
Epoch 34: val_accuracy did not improve from 0.65240

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.6241 - loss: 0.9030 - val_accuracy: 0.6409 - val_loss: 0.8619 - learning_rate: 4.0000e-05
Epoch 34: early stopping
Restoring model weights from the end of the best epoch: 27.

Plotting the Training and Validation Accuracies¶

In [39]:
plt.plot(history_vgg.history["accuracy"])
plt.plot(history_vgg.history["val_accuracy"])
plt.title("VGG16 Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the VGG16 model¶

In [40]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_vgg16.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = new_vgg16_model.evaluate(test_generator_vgg16, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.8750 - loss: 0.5965

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.6979 - loss: 0.7874 
Loss: 0.8395289778709412, Accuracy: 0.65625

Plotting Confusion Matrix¶

In [41]:
pred_probabilities = new_vgg16_model.predict(test_generator_vgg16, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator_vgg16.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("VGG16 Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 461ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step  
              precision    recall  f1-score   support

       happy       0.64      0.88      0.74        32
     neutral       0.54      0.44      0.48        32
         sad       0.61      0.62      0.62        32
    surprise       0.88      0.69      0.77        32

    accuracy                           0.66       128
   macro avg       0.67      0.66      0.65       128
weighted avg       0.67      0.66      0.65       128

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Think About It:

  • What do you infer from the general trend in the training performance?
  • Is the training accuracy consistently improving?
  • Is the validation accuracy also improving similarly?

Observations and Insights:

  • Utilizing the VGG16 architecture, the model has a total of 1,735,488; however, they are not trainable due to freezing the earlier layers.
  • The model achieved an accuracy of 65.62%, which is less compared to the earlier CNN models, implying that the transfer learning approach may not have been as effective in this particular case.
  • In terms of individual class performance, the model performed best on the 'happy' and 'surprise' emotions with an f1-score of 0.77 and 0.74, showing strong recognition capabilities for these emotions.
  • The class 'sad' saw moderate f1-score of 0.62, whereas 'neutral' had the lowest f1-score of 0.48, indicating that the model struggles more with neutral expressions and general differentiation between these emotions.

This analysis suggests that while leveraging a pre-trained network like VGG16 brings in advanced feature detection capabilities, for the specific case of small grayscale images representing facial emotions, the intricate features learned by VGG16 from large-scale colored image datasets may not fully translate, thereby not offering a substantial advantage over simpler, tailored CNN architectures.

Note: You can even go back and build your own architecture on top of the VGG16 Transfer layer and see if you can improve the performance

  • We have tried to improve the model by changing the learning rate, adding more layers, changing the number of units in the layers, and changing the optimizer.

ResNet V2 Model¶

In [42]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [43]:
resnet_model = ResNet50V2(weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers))
resnet_model.summary()
Model: "resnet50v2"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ input_layer         │ (None, 48, 48, 3) │          0 │ -                 │
│ (InputLayer)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv1_pad           │ (None, 54, 54, 3) │          0 │ input_layer[0][0] │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv1_conv (Conv2D) │ (None, 24, 24,    │      9,472 │ conv1_pad[0][0]   │
│                     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ pool1_pad           │ (None, 26, 26,    │          0 │ conv1_conv[0][0]  │
│ (ZeroPadding2D)     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ pool1_pool          │ (None, 12, 12,    │          0 │ pool1_pad[0][0]   │
│ (MaxPooling2D)      │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_preac… │ (None, 12, 12,    │        256 │ pool1_pool[0][0]  │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_preac… │ (None, 12, 12,    │          0 │ conv2_block1_pre… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_1_conv │ (None, 12, 12,    │      4,096 │ conv2_block1_pre… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_1_bn   │ (None, 12, 12,    │        256 │ conv2_block1_1_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_1_relu │ (None, 12, 12,    │          0 │ conv2_block1_1_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_2_pad  │ (None, 14, 14,    │          0 │ conv2_block1_1_r… │
│ (ZeroPadding2D)     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_2_conv │ (None, 12, 12,    │     36,864 │ conv2_block1_2_p… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_2_bn   │ (None, 12, 12,    │        256 │ conv2_block1_2_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_2_relu │ (None, 12, 12,    │          0 │ conv2_block1_2_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_0_conv │ (None, 12, 12,    │     16,640 │ conv2_block1_pre… │
│ (Conv2D)            │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_3_conv │ (None, 12, 12,    │     16,640 │ conv2_block1_2_r… │
│ (Conv2D)            │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block1_out    │ (None, 12, 12,    │          0 │ conv2_block1_0_c… │
│ (Add)               │ 256)              │            │ conv2_block1_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_preac… │ (None, 12, 12,    │      1,024 │ conv2_block1_out… │
│ (BatchNormalizatio… │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_preac… │ (None, 12, 12,    │          0 │ conv2_block2_pre… │
│ (Activation)        │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_1_conv │ (None, 12, 12,    │     16,384 │ conv2_block2_pre… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_1_bn   │ (None, 12, 12,    │        256 │ conv2_block2_1_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_1_relu │ (None, 12, 12,    │          0 │ conv2_block2_1_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_2_pad  │ (None, 14, 14,    │          0 │ conv2_block2_1_r… │
│ (ZeroPadding2D)     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_2_conv │ (None, 12, 12,    │     36,864 │ conv2_block2_2_p… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_2_bn   │ (None, 12, 12,    │        256 │ conv2_block2_2_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_2_relu │ (None, 12, 12,    │          0 │ conv2_block2_2_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_3_conv │ (None, 12, 12,    │     16,640 │ conv2_block2_2_r… │
│ (Conv2D)            │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block2_out    │ (None, 12, 12,    │          0 │ conv2_block1_out… │
│ (Add)               │ 256)              │            │ conv2_block2_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_preac… │ (None, 12, 12,    │      1,024 │ conv2_block2_out… │
│ (BatchNormalizatio… │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_preac… │ (None, 12, 12,    │          0 │ conv2_block3_pre… │
│ (Activation)        │ 256)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_1_conv │ (None, 12, 12,    │     16,384 │ conv2_block3_pre… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_1_bn   │ (None, 12, 12,    │        256 │ conv2_block3_1_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_1_relu │ (None, 12, 12,    │          0 │ conv2_block3_1_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_2_pad  │ (None, 14, 14,    │          0 │ conv2_block3_1_r… │
│ (ZeroPadding2D)     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_2_conv │ (None, 6, 6, 64)  │     36,864 │ conv2_block3_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_2_bn   │ (None, 6, 6, 64)  │        256 │ conv2_block3_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_2_relu │ (None, 6, 6, 64)  │          0 │ conv2_block3_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ max_pooling2d       │ (None, 6, 6, 256) │          0 │ conv2_block2_out… │
│ (MaxPooling2D)      │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_3_conv │ (None, 6, 6, 256) │     16,640 │ conv2_block3_2_r… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2_block3_out    │ (None, 6, 6, 256) │          0 │ max_pooling2d[0]… │
│ (Add)               │                   │            │ conv2_block3_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_preac… │ (None, 6, 6, 256) │      1,024 │ conv2_block3_out… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_preac… │ (None, 6, 6, 256) │          0 │ conv3_block1_pre… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_1_conv │ (None, 6, 6, 128) │     32,768 │ conv3_block1_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_1_bn   │ (None, 6, 6, 128) │        512 │ conv3_block1_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_1_relu │ (None, 6, 6, 128) │          0 │ conv3_block1_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_2_pad  │ (None, 8, 8, 128) │          0 │ conv3_block1_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_2_conv │ (None, 6, 6, 128) │    147,456 │ conv3_block1_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_2_bn   │ (None, 6, 6, 128) │        512 │ conv3_block1_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_2_relu │ (None, 6, 6, 128) │          0 │ conv3_block1_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_0_conv │ (None, 6, 6, 512) │    131,584 │ conv3_block1_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_3_conv │ (None, 6, 6, 512) │     66,048 │ conv3_block1_2_r… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block1_out    │ (None, 6, 6, 512) │          0 │ conv3_block1_0_c… │
│ (Add)               │                   │            │ conv3_block1_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_preac… │ (None, 6, 6, 512) │      2,048 │ conv3_block1_out… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_preac… │ (None, 6, 6, 512) │          0 │ conv3_block2_pre… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_1_conv │ (None, 6, 6, 128) │     65,536 │ conv3_block2_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_1_bn   │ (None, 6, 6, 128) │        512 │ conv3_block2_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_1_relu │ (None, 6, 6, 128) │          0 │ conv3_block2_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_2_pad  │ (None, 8, 8, 128) │          0 │ conv3_block2_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_2_conv │ (None, 6, 6, 128) │    147,456 │ conv3_block2_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_2_bn   │ (None, 6, 6, 128) │        512 │ conv3_block2_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_2_relu │ (None, 6, 6, 128) │          0 │ conv3_block2_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_3_conv │ (None, 6, 6, 512) │     66,048 │ conv3_block2_2_r… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block2_out    │ (None, 6, 6, 512) │          0 │ conv3_block1_out… │
│ (Add)               │                   │            │ conv3_block2_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_preac… │ (None, 6, 6, 512) │      2,048 │ conv3_block2_out… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_preac… │ (None, 6, 6, 512) │          0 │ conv3_block3_pre… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_1_conv │ (None, 6, 6, 128) │     65,536 │ conv3_block3_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_1_bn   │ (None, 6, 6, 128) │        512 │ conv3_block3_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_1_relu │ (None, 6, 6, 128) │          0 │ conv3_block3_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_2_pad  │ (None, 8, 8, 128) │          0 │ conv3_block3_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_2_conv │ (None, 6, 6, 128) │    147,456 │ conv3_block3_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_2_bn   │ (None, 6, 6, 128) │        512 │ conv3_block3_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_2_relu │ (None, 6, 6, 128) │          0 │ conv3_block3_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_3_conv │ (None, 6, 6, 512) │     66,048 │ conv3_block3_2_r… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block3_out    │ (None, 6, 6, 512) │          0 │ conv3_block2_out… │
│ (Add)               │                   │            │ conv3_block3_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_preac… │ (None, 6, 6, 512) │      2,048 │ conv3_block3_out… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_preac… │ (None, 6, 6, 512) │          0 │ conv3_block4_pre… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_1_conv │ (None, 6, 6, 128) │     65,536 │ conv3_block4_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_1_bn   │ (None, 6, 6, 128) │        512 │ conv3_block4_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_1_relu │ (None, 6, 6, 128) │          0 │ conv3_block4_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_2_pad  │ (None, 8, 8, 128) │          0 │ conv3_block4_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_2_conv │ (None, 3, 3, 128) │    147,456 │ conv3_block4_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_2_bn   │ (None, 3, 3, 128) │        512 │ conv3_block4_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_2_relu │ (None, 3, 3, 128) │          0 │ conv3_block4_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ max_pooling2d_1     │ (None, 3, 3, 512) │          0 │ conv3_block3_out… │
│ (MaxPooling2D)      │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_3_conv │ (None, 3, 3, 512) │     66,048 │ conv3_block4_2_r… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv3_block4_out    │ (None, 3, 3, 512) │          0 │ max_pooling2d_1[… │
│ (Add)               │                   │            │ conv3_block4_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_preac… │ (None, 3, 3, 512) │      2,048 │ conv3_block4_out… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_preac… │ (None, 3, 3, 512) │          0 │ conv4_block1_pre… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_1_conv │ (None, 3, 3, 256) │    131,072 │ conv4_block1_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block1_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block1_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block1_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_2_conv │ (None, 3, 3, 256) │    589,824 │ conv4_block1_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_2_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block1_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_2_relu │ (None, 3, 3, 256) │          0 │ conv4_block1_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_0_conv │ (None, 3, 3,      │    525,312 │ conv4_block1_pre… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_3_conv │ (None, 3, 3,      │    263,168 │ conv4_block1_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block1_out    │ (None, 3, 3,      │          0 │ conv4_block1_0_c… │
│ (Add)               │ 1024)             │            │ conv4_block1_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_preac… │ (None, 3, 3,      │      4,096 │ conv4_block1_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_preac… │ (None, 3, 3,      │          0 │ conv4_block2_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_1_conv │ (None, 3, 3, 256) │    262,144 │ conv4_block2_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block2_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block2_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block2_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_2_conv │ (None, 3, 3, 256) │    589,824 │ conv4_block2_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_2_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block2_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_2_relu │ (None, 3, 3, 256) │          0 │ conv4_block2_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_3_conv │ (None, 3, 3,      │    263,168 │ conv4_block2_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block2_out    │ (None, 3, 3,      │          0 │ conv4_block1_out… │
│ (Add)               │ 1024)             │            │ conv4_block2_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_preac… │ (None, 3, 3,      │      4,096 │ conv4_block2_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_preac… │ (None, 3, 3,      │          0 │ conv4_block3_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_1_conv │ (None, 3, 3, 256) │    262,144 │ conv4_block3_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block3_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block3_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block3_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_2_conv │ (None, 3, 3, 256) │    589,824 │ conv4_block3_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_2_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block3_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_2_relu │ (None, 3, 3, 256) │          0 │ conv4_block3_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_3_conv │ (None, 3, 3,      │    263,168 │ conv4_block3_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block3_out    │ (None, 3, 3,      │          0 │ conv4_block2_out… │
│ (Add)               │ 1024)             │            │ conv4_block3_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_preac… │ (None, 3, 3,      │      4,096 │ conv4_block3_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_preac… │ (None, 3, 3,      │          0 │ conv4_block4_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_1_conv │ (None, 3, 3, 256) │    262,144 │ conv4_block4_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block4_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block4_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block4_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_2_conv │ (None, 3, 3, 256) │    589,824 │ conv4_block4_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_2_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block4_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_2_relu │ (None, 3, 3, 256) │          0 │ conv4_block4_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_3_conv │ (None, 3, 3,      │    263,168 │ conv4_block4_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block4_out    │ (None, 3, 3,      │          0 │ conv4_block3_out… │
│ (Add)               │ 1024)             │            │ conv4_block4_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_preac… │ (None, 3, 3,      │      4,096 │ conv4_block4_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_preac… │ (None, 3, 3,      │          0 │ conv4_block5_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_1_conv │ (None, 3, 3, 256) │    262,144 │ conv4_block5_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block5_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block5_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block5_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_2_conv │ (None, 3, 3, 256) │    589,824 │ conv4_block5_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_2_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block5_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_2_relu │ (None, 3, 3, 256) │          0 │ conv4_block5_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_3_conv │ (None, 3, 3,      │    263,168 │ conv4_block5_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block5_out    │ (None, 3, 3,      │          0 │ conv4_block4_out… │
│ (Add)               │ 1024)             │            │ conv4_block5_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_preac… │ (None, 3, 3,      │      4,096 │ conv4_block5_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_preac… │ (None, 3, 3,      │          0 │ conv4_block6_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_1_conv │ (None, 3, 3, 256) │    262,144 │ conv4_block6_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_1_bn   │ (None, 3, 3, 256) │      1,024 │ conv4_block6_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_1_relu │ (None, 3, 3, 256) │          0 │ conv4_block6_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_2_pad  │ (None, 5, 5, 256) │          0 │ conv4_block6_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_2_conv │ (None, 2, 2, 256) │    589,824 │ conv4_block6_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_2_bn   │ (None, 2, 2, 256) │      1,024 │ conv4_block6_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_2_relu │ (None, 2, 2, 256) │          0 │ conv4_block6_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ max_pooling2d_2     │ (None, 2, 2,      │          0 │ conv4_block5_out… │
│ (MaxPooling2D)      │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_3_conv │ (None, 2, 2,      │    263,168 │ conv4_block6_2_r… │
│ (Conv2D)            │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv4_block6_out    │ (None, 2, 2,      │          0 │ max_pooling2d_2[… │
│ (Add)               │ 1024)             │            │ conv4_block6_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_preac… │ (None, 2, 2,      │      4,096 │ conv4_block6_out… │
│ (BatchNormalizatio… │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_preac… │ (None, 2, 2,      │          0 │ conv5_block1_pre… │
│ (Activation)        │ 1024)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_1_conv │ (None, 2, 2, 512) │    524,288 │ conv5_block1_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_1_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block1_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_1_relu │ (None, 2, 2, 512) │          0 │ conv5_block1_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_2_pad  │ (None, 4, 4, 512) │          0 │ conv5_block1_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_2_conv │ (None, 2, 2, 512) │  2,359,296 │ conv5_block1_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_2_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block1_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_2_relu │ (None, 2, 2, 512) │          0 │ conv5_block1_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_0_conv │ (None, 2, 2,      │  2,099,200 │ conv5_block1_pre… │
│ (Conv2D)            │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_3_conv │ (None, 2, 2,      │  1,050,624 │ conv5_block1_2_r… │
│ (Conv2D)            │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block1_out    │ (None, 2, 2,      │          0 │ conv5_block1_0_c… │
│ (Add)               │ 2048)             │            │ conv5_block1_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_preac… │ (None, 2, 2,      │      8,192 │ conv5_block1_out… │
│ (BatchNormalizatio… │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_preac… │ (None, 2, 2,      │          0 │ conv5_block2_pre… │
│ (Activation)        │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_1_conv │ (None, 2, 2, 512) │  1,048,576 │ conv5_block2_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_1_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block2_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_1_relu │ (None, 2, 2, 512) │          0 │ conv5_block2_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_2_pad  │ (None, 4, 4, 512) │          0 │ conv5_block2_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_2_conv │ (None, 2, 2, 512) │  2,359,296 │ conv5_block2_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_2_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block2_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_2_relu │ (None, 2, 2, 512) │          0 │ conv5_block2_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_3_conv │ (None, 2, 2,      │  1,050,624 │ conv5_block2_2_r… │
│ (Conv2D)            │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block2_out    │ (None, 2, 2,      │          0 │ conv5_block1_out… │
│ (Add)               │ 2048)             │            │ conv5_block2_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_preac… │ (None, 2, 2,      │      8,192 │ conv5_block2_out… │
│ (BatchNormalizatio… │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_preac… │ (None, 2, 2,      │          0 │ conv5_block3_pre… │
│ (Activation)        │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_1_conv │ (None, 2, 2, 512) │  1,048,576 │ conv5_block3_pre… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_1_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block3_1_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_1_relu │ (None, 2, 2, 512) │          0 │ conv5_block3_1_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_2_pad  │ (None, 4, 4, 512) │          0 │ conv5_block3_1_r… │
│ (ZeroPadding2D)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_2_conv │ (None, 2, 2, 512) │  2,359,296 │ conv5_block3_2_p… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_2_bn   │ (None, 2, 2, 512) │      2,048 │ conv5_block3_2_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_2_relu │ (None, 2, 2, 512) │          0 │ conv5_block3_2_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_3_conv │ (None, 2, 2,      │  1,050,624 │ conv5_block3_2_r… │
│ (Conv2D)            │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv5_block3_out    │ (None, 2, 2,      │          0 │ conv5_block2_out… │
│ (Add)               │ 2048)             │            │ conv5_block3_3_c… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ post_bn             │ (None, 2, 2,      │      8,192 │ conv5_block3_out… │
│ (BatchNormalizatio… │ 2048)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ post_relu           │ (None, 2, 2,      │          0 │ post_bn[0][0]     │
│ (Activation)        │ 2048)             │            │                   │
└─────────────────────┴───────────────────┴────────────┴───────────────────┘
 Total params: 23,564,800 (89.89 MB)
 Trainable params: 23,519,360 (89.72 MB)
 Non-trainable params: 45,440 (177.50 KB)

Model Building¶

  • Import Resnet v2 upto the layer of your choice and add Fully Connected layers on top of it.
In [44]:
# Define a new model that cuts ResNet50V2 at the 'conv3_block4_out' layer
model_output = resnet_model.get_layer("conv3_block4_out").output
cut_model = Model(inputs=resnet_model.input, outputs=model_output)

# Freezing the layers
for layer in resnet_model.layers:
    layer.trainable = False
In [45]:
new_resnet_model = Sequential()
new_resnet_model.add(cut_model)

# Reduces each feature map to a single value by averaging all elements
new_resnet_model.add(GlobalAveragePooling2D())

# Adding full connected layers
new_resnet_model.add(Dense(256, activation="relu"))
# Adding output layer
new_resnet_model.add(Dense(4, activation="softmax"))

# Using Adam Optimizer
optimizer = Adam(learning_rate=0.001)

Compiling and Training the Model¶

In [46]:
new_resnet_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_resnet_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ functional_1 (Functional)       │ ?                      │     1,453,568 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d        │ ?                      │   0 (unbuilt) │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ ?                      │   0 (unbuilt) │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 1,453,568 (5.54 MB)
 Trainable params: 0 (0.00 B)
 Non-trainable params: 1,453,568 (5.54 MB)
In [47]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

mc = ModelCheckpoint(
    f"{results_path}/best_model_resnet_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 40 epochs and using validation set
history_resnet = new_resnet_model.fit(
    train_generator_resnet,
    epochs=40,
    validation_data=validation_generator_resnet,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
I0000 00:00:1712794843.982897 1499815 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 4 bytes spill stores, 4 bytes spill loads

I0000 00:00:1712794844.285459 1499816 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 32 bytes spill stores, 32 bytes spill loads

I0000 00:00:1712794844.474295 1499820 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_3697', 40 bytes spill stores, 40 bytes spill loads

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Epoch 1: val_accuracy improved from -inf to 0.48081, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 21s 34ms/step - accuracy: 0.4011 - loss: 1.3650 - val_accuracy: 0.4808 - val_loss: 1.1593 - learning_rate: 0.0010
Epoch 2/40
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 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4982 - loss: 1.1460

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Epoch 2: val_accuracy improved from 0.48081 to 0.54330, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.4816 - loss: 1.1602 - val_accuracy: 0.5433 - val_loss: 1.0592 - learning_rate: 0.0010
Epoch 3/40
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273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.4969 - loss: 1.1424

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.4969 - loss: 1.1424

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.4969 - loss: 1.1423

282/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.4969 - loss: 1.1422

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4968 - loss: 1.1422

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4968 - loss: 1.1421

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4968 - loss: 1.1420

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4968 - loss: 1.1420

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4968 - loss: 1.1419

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1419

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1418

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1418

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1417

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1417

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1416

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1416

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1416

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4967 - loss: 1.1415

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4966 - loss: 1.1415

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4966 - loss: 1.1415

330/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.4966 - loss: 1.1414

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4966 - loss: 1.1414

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4966 - loss: 1.1414

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4966 - loss: 1.1413

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4966 - loss: 1.1413

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1413

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1413

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1413

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1412

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1412

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1412

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4965 - loss: 1.1412

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4964 - loss: 1.1412

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4964 - loss: 1.1412

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4964 - loss: 1.1412

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4964 - loss: 1.1412

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4964 - loss: 1.1412

378/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.4963 - loss: 1.1412

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1411

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1410

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1410

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1410

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1410

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4963 - loss: 1.1410

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1410

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1409

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.4962 - loss: 1.1409

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4962 - loss: 1.1409

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4961 - loss: 1.1409

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4960 - loss: 1.1409

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4960 - loss: 1.1409
Epoch 3: val_accuracy did not improve from 0.54330

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.4960 - loss: 1.1408 - val_accuracy: 0.5252 - val_loss: 1.0921 - learning_rate: 0.0010
Epoch 4/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 174ms/step - accuracy: 0.5625 - loss: 1.0699

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4714 - loss: 1.1716   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4815 - loss: 1.1635

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4851 - loss: 1.1602

 11/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.4891 - loss: 1.1554

 14/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.4919 - loss: 1.1507

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4929 - loss: 1.1481

 20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.4944 - loss: 1.1441

 23/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4960 - loss: 1.1395 

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4963 - loss: 1.1363

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4964 - loss: 1.1350

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4967 - loss: 1.1343

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4969 - loss: 1.1340

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4969 - loss: 1.1342

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4970 - loss: 1.1340

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4972 - loss: 1.1337

 45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4975 - loss: 1.1332

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4981 - loss: 1.1326

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4988 - loss: 1.1320

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4998 - loss: 1.1311

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5006 - loss: 1.1303

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5015 - loss: 1.1296

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5023 - loss: 1.1287

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5030 - loss: 1.1279

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5037 - loss: 1.1271

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5043 - loss: 1.1264

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5048 - loss: 1.1258

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5051 - loss: 1.1253

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5052 - loss: 1.1251

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5053 - loss: 1.1248

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1245

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1243

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5055 - loss: 1.1243

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5054 - loss: 1.1242

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1242

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1241

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1240

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1239

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1238

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1237

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1236

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1236

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1235

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1235

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5054 - loss: 1.1234

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5055 - loss: 1.1232

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5056 - loss: 1.1230

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5057 - loss: 1.1227

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5058 - loss: 1.1225

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5060 - loss: 1.1223

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5061 - loss: 1.1220

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5062 - loss: 1.1218

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Epoch 4: val_accuracy improved from 0.54330 to 0.55375, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5073 - loss: 1.1203 - val_accuracy: 0.5537 - val_loss: 1.0311 - learning_rate: 0.0010
Epoch 5/40
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400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5018 - loss: 1.1265

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5018 - loss: 1.1264

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5019 - loss: 1.1264

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5019 - loss: 1.1263

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1263

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1262

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5020 - loss: 1.1262

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5021 - loss: 1.1261

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5021 - loss: 1.1260

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5021 - loss: 1.1260

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5022 - loss: 1.1259

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1258

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1258

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5023 - loss: 1.1257

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1257

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1256

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5024 - loss: 1.1256

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1255

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1255

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5025 - loss: 1.1254

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5026 - loss: 1.1254

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5026 - loss: 1.1253

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5027 - loss: 1.1252
Epoch 5: val_accuracy did not improve from 0.55375

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5027 - loss: 1.1252 - val_accuracy: 0.5409 - val_loss: 1.0454 - learning_rate: 0.0010
Epoch 6/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:49 232ms/step - accuracy: 0.3750 - loss: 1.3090

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4753 - loss: 1.2070   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5107 - loss: 1.1654

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5251 - loss: 1.1468

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5392 - loss: 1.1267

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5424 - loss: 1.1171 

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5440 - loss: 1.1111

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5449 - loss: 1.1075

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5446 - loss: 1.1045

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5440 - loss: 1.1037

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5431 - loss: 1.1023

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5420 - loss: 1.1012

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5411 - loss: 1.1003

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5404 - loss: 1.0998

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5399 - loss: 1.0992

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5393 - loss: 1.0981

 45/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5388 - loss: 1.0974

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5383 - loss: 1.0970

 51/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5380 - loss: 1.0965

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5375 - loss: 1.0960

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5371 - loss: 1.0956

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5367 - loss: 1.0951

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5364 - loss: 1.0946

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5359 - loss: 1.0942

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5354 - loss: 1.0939

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5348 - loss: 1.0937

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5343 - loss: 1.0934

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5338 - loss: 1.0932

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5333 - loss: 1.0930

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5330 - loss: 1.0930

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5326 - loss: 1.0928

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5321 - loss: 1.0927

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5317 - loss: 1.0927

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5314 - loss: 1.0926

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5310 - loss: 1.0925

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5307 - loss: 1.0925

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5305 - loss: 1.0924

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5301 - loss: 1.0924

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5297 - loss: 1.0923

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5294 - loss: 1.0923

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5290 - loss: 1.0923

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5287 - loss: 1.0922

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5285 - loss: 1.0921

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5283 - loss: 1.0920

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5280 - loss: 1.0919

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5278 - loss: 1.0918

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5276 - loss: 1.0917

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5273 - loss: 1.0917

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5271 - loss: 1.0916

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5270 - loss: 1.0915

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5268 - loss: 1.0915

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5266 - loss: 1.0915

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5265 - loss: 1.0914

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5263 - loss: 1.0914

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5261 - loss: 1.0914

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5259 - loss: 1.0913

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5258 - loss: 1.0913

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5256 - loss: 1.0913

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5255 - loss: 1.0912

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5254 - loss: 1.0911

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5253 - loss: 1.0910

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5252 - loss: 1.0910

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5252 - loss: 1.0910

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5251 - loss: 1.0909

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5250 - loss: 1.0908

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5249 - loss: 1.0908

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5248 - loss: 1.0907

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5248 - loss: 1.0906

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5247 - loss: 1.0906

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5246 - loss: 1.0906

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5245 - loss: 1.0906

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5244 - loss: 1.0906

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5243 - loss: 1.0907

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5243 - loss: 1.0907

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5242 - loss: 1.0907

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5241 - loss: 1.0907

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5240 - loss: 1.0907

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5239 - loss: 1.0907

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5238 - loss: 1.0908

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5237 - loss: 1.0908

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5236 - loss: 1.0909

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5236 - loss: 1.0909

235/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5235 - loss: 1.0909

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5234 - loss: 1.0910

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5233 - loss: 1.0910

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5231 - loss: 1.0911

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5231 - loss: 1.0911

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5229 - loss: 1.0912

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5229 - loss: 1.0913

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5228 - loss: 1.0913

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5227 - loss: 1.0914

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5226 - loss: 1.0915

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5225 - loss: 1.0915

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5224 - loss: 1.0916

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5223 - loss: 1.0917

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Epoch 6: val_accuracy improved from 0.55375 to 0.55917, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

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Epoch 7/40
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159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5177 - loss: 1.0831

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5177 - loss: 1.0831

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5178 - loss: 1.0832

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5178 - loss: 1.0832

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173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5179 - loss: 1.0833

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178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5179 - loss: 1.0834

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5179 - loss: 1.0835

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5179 - loss: 1.0836

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0836

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0837

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0838

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0838

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0839

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5179 - loss: 1.0839

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5180 - loss: 1.0839

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5180 - loss: 1.0840

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5180 - loss: 1.0841

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5180 - loss: 1.0841

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5181 - loss: 1.0841

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5181 - loss: 1.0842

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5182 - loss: 1.0842

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5182 - loss: 1.0843

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5182 - loss: 1.0843

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5183 - loss: 1.0843

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5183 - loss: 1.0844

234/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5183 - loss: 1.0844

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5184 - loss: 1.0844

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5184 - loss: 1.0845

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5184 - loss: 1.0845

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5185 - loss: 1.0846

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5185 - loss: 1.0847

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5185 - loss: 1.0847

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5186 - loss: 1.0848

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5186 - loss: 1.0849

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5187 - loss: 1.0850

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5187 - loss: 1.0850

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5187 - loss: 1.0851

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5188 - loss: 1.0851

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5188 - loss: 1.0852

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5188 - loss: 1.0852

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5189 - loss: 1.0853

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5189 - loss: 1.0853

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5189 - loss: 1.0854

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5189 - loss: 1.0855

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5190 - loss: 1.0855

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5190 - loss: 1.0856

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5190 - loss: 1.0857

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5190 - loss: 1.0858

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5190 - loss: 1.0858

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5191 - loss: 1.0859

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5191 - loss: 1.0860

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5191 - loss: 1.0860

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0861

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0862

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0862

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0863

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0864

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0864

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5192 - loss: 1.0865

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5192 - loss: 1.0866

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5192 - loss: 1.0867

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0868

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0868

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0869

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0870

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0870

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0871

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0872

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0873

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0873

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0874

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0875

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5193 - loss: 1.0875

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0876

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0876

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5194 - loss: 1.0877

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0877

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0878

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0878

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5194 - loss: 1.0879

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0879

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0880

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0880

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0881

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0881

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0882

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0882

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0883

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0883

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0884

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0884

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0885

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0885

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5195 - loss: 1.0886

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0886

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0887

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0887

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0888

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0888

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0889

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0889

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0890

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0891

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0891

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0892

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5195 - loss: 1.0893
Epoch 7: val_accuracy did not improve from 0.55917

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5196 - loss: 1.0894 - val_accuracy: 0.5535 - val_loss: 1.0247 - learning_rate: 0.0010
Epoch 8/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.6250 - loss: 0.9302

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6309 - loss: 0.9532   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6083 - loss: 0.9931

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5945 - loss: 1.0163

 11/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.5836 - loss: 1.0342

 14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5713 - loss: 1.0511

 17/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5610 - loss: 1.0636

 20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5546 - loss: 1.0714

 23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5504 - loss: 1.0760

 26/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5476 - loss: 1.0793

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5451 - loss: 1.0816 

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397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0971

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0970

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0969

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0968

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5280 - loss: 1.0968

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0968

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0967

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0966

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0966

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5279 - loss: 1.0965

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0964

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0964

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0963

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0963

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0962

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0962

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5278 - loss: 1.0961
Epoch 8: val_accuracy did not improve from 0.55917

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5278 - loss: 1.0961 - val_accuracy: 0.5341 - val_loss: 1.0665 - learning_rate: 0.0010
Epoch 9/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5000 - loss: 1.1318

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5208 - loss: 1.1205   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5170 - loss: 1.1355

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5140 - loss: 1.1395

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5143 - loss: 1.1339

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5152 - loss: 1.1290

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5167 - loss: 1.1240

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5172 - loss: 1.1215

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5187 - loss: 1.1177

 27/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5197 - loss: 1.1149

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5208 - loss: 1.1121

 32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5215 - loss: 1.1102

 35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5221 - loss: 1.1084

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5227 - loss: 1.1072

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5231 - loss: 1.1064

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5234 - loss: 1.1057

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5237 - loss: 1.1051

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5238 - loss: 1.1047

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5240 - loss: 1.1043

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5241 - loss: 1.1038

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5243 - loss: 1.1033

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5244 - loss: 1.1029

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5245 - loss: 1.1023

 68/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5247 - loss: 1.1018

 71/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5248 - loss: 1.1013

 74/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5251 - loss: 1.1006

 77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5255 - loss: 1.0998

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5258 - loss: 1.0994

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5260 - loss: 1.0989

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5262 - loss: 1.0985

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5263 - loss: 1.0982

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5265 - loss: 1.0978

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5268 - loss: 1.0973

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5272 - loss: 1.0967

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5275 - loss: 1.0960

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5278 - loss: 1.0953

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5280 - loss: 1.0949

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5283 - loss: 1.0944

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5285 - loss: 1.0939

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5288 - loss: 1.0934

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5290 - loss: 1.0930

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5292 - loss: 1.0926

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5294 - loss: 1.0923

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5295 - loss: 1.0920

127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5296 - loss: 1.0916

130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5298 - loss: 1.0913

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5299 - loss: 1.0911

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5300 - loss: 1.0908

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5301 - loss: 1.0905

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5302 - loss: 1.0903

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5303 - loss: 1.0901

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5304 - loss: 1.0899

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5305 - loss: 1.0896

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5306 - loss: 1.0892

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5307 - loss: 1.0888

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5308 - loss: 1.0885

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5308 - loss: 1.0882

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5309 - loss: 1.0880

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5310 - loss: 1.0877

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5310 - loss: 1.0875

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5311 - loss: 1.0873

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5311 - loss: 1.0872

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5312 - loss: 1.0870

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0868

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0867

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0866

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0865

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0864

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0863

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5314 - loss: 1.0862

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5315 - loss: 1.0861

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5315 - loss: 1.0860

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5315 - loss: 1.0860

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5315 - loss: 1.0859

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0858

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0857

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0856

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0855

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0855

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0854

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0854

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0853

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5316 - loss: 1.0853

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0852

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0851

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0851

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0850

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0849

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0849

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0848

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0847

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5315 - loss: 1.0847

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5314 - loss: 1.0847

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5314 - loss: 1.0846

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5314 - loss: 1.0846

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5314 - loss: 1.0846

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5313 - loss: 1.0845

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5313 - loss: 1.0845

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5313 - loss: 1.0845

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5313 - loss: 1.0844

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5313 - loss: 1.0844

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5313 - loss: 1.0843

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5312 - loss: 1.0843

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5312 - loss: 1.0843

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5312 - loss: 1.0843

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5312 - loss: 1.0843

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5311 - loss: 1.0842

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5311 - loss: 1.0843

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5311 - loss: 1.0843

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5310 - loss: 1.0843

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5310 - loss: 1.0843

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5309 - loss: 1.0843

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5309 - loss: 1.0843

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5308 - loss: 1.0843

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5308 - loss: 1.0844

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5307 - loss: 1.0844

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5307 - loss: 1.0844

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5306 - loss: 1.0844

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5306 - loss: 1.0844

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5305 - loss: 1.0844

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5305 - loss: 1.0844

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5304 - loss: 1.0844

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5304 - loss: 1.0844

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5304 - loss: 1.0845

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5303 - loss: 1.0845

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5303 - loss: 1.0845

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5302 - loss: 1.0845

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5302 - loss: 1.0845

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5302 - loss: 1.0845

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5301 - loss: 1.0846

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5301 - loss: 1.0846

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5301 - loss: 1.0846

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5300 - loss: 1.0846

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5300 - loss: 1.0847

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5299 - loss: 1.0847

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5299 - loss: 1.0848

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5298 - loss: 1.0848

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5298 - loss: 1.0849

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5298 - loss: 1.0849

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5297 - loss: 1.0849

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5297 - loss: 1.0849

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5296 - loss: 1.0850

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5296 - loss: 1.0850

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5296 - loss: 1.0850

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5295 - loss: 1.0850

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5295 - loss: 1.0851

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5295 - loss: 1.0851

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5294 - loss: 1.0851

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5294 - loss: 1.0851

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5293 - loss: 1.0852

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5293 - loss: 1.0852

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5293 - loss: 1.0852

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5292 - loss: 1.0853

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0853

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0854

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5291 - loss: 1.0854

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5290 - loss: 1.0854

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5289 - loss: 1.0855

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5288 - loss: 1.0855

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5288 - loss: 1.0856
Epoch 9: val_accuracy did not improve from 0.55917

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5288 - loss: 1.0856 - val_accuracy: 0.5357 - val_loss: 1.0625 - learning_rate: 0.0010
Epoch 10/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 173ms/step - accuracy: 0.4375 - loss: 1.2522

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4635 - loss: 1.1908   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4919 - loss: 1.1545

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5098 - loss: 1.1335

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5185 - loss: 1.1224

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5253 - loss: 1.1132

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5301 - loss: 1.1058

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5329 - loss: 1.1007

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5342 - loss: 1.0979

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5355 - loss: 1.0957

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5369 - loss: 1.0932

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5380 - loss: 1.0908

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5387 - loss: 1.0890

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5390 - loss: 1.0873

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0858

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0850

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0846

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0839

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0835

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5391 - loss: 1.0831

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5392 - loss: 1.0826

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5393 - loss: 1.0821

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5393 - loss: 1.0818

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5396 - loss: 1.0814

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5399 - loss: 1.0809

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5402 - loss: 1.0805

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5404 - loss: 1.0802

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5406 - loss: 1.0800

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5407 - loss: 1.0799

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5408 - loss: 1.0798

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5409 - loss: 1.0797

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5411 - loss: 1.0797

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5413 - loss: 1.0797

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5415 - loss: 1.0796

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5418 - loss: 1.0794

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5420 - loss: 1.0793

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5423 - loss: 1.0791

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5425 - loss: 1.0790

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5428 - loss: 1.0787

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5431 - loss: 1.0785

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5433 - loss: 1.0782

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5435 - loss: 1.0781

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5436 - loss: 1.0779

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5438 - loss: 1.0778

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0776

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5441 - loss: 1.0775

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5442 - loss: 1.0773

133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5444 - loss: 1.0771

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5445 - loss: 1.0769

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5446 - loss: 1.0766

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5447 - loss: 1.0765

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5447 - loss: 1.0763

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5448 - loss: 1.0760

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153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5449 - loss: 1.0757

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5449 - loss: 1.0755

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5450 - loss: 1.0753

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164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5450 - loss: 1.0751

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0749

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0748

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0747

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0746

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0745

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5451 - loss: 1.0745

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0744

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0743

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5450 - loss: 1.0743

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5449 - loss: 1.0742

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5449 - loss: 1.0742

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5448 - loss: 1.0741

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5448 - loss: 1.0741

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5447 - loss: 1.0740

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5447 - loss: 1.0740

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5446 - loss: 1.0739

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5445 - loss: 1.0738

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5444 - loss: 1.0737

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0737

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0736

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0735

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0735

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0734

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0733

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0733

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0732

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0732

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5444 - loss: 1.0731

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0730

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0729

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5443 - loss: 1.0729

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5442 - loss: 1.0728

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5442 - loss: 1.0728

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5441 - loss: 1.0728

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5441 - loss: 1.0727

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5440 - loss: 1.0727

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5440 - loss: 1.0727

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5439 - loss: 1.0726

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5439 - loss: 1.0726

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5438 - loss: 1.0726

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5438 - loss: 1.0726

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5437 - loss: 1.0726

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5437 - loss: 1.0725

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5436 - loss: 1.0725

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5436 - loss: 1.0725

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5435 - loss: 1.0725

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5434 - loss: 1.0725

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5434 - loss: 1.0725

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5433 - loss: 1.0725

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5433 - loss: 1.0725

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5432 - loss: 1.0725

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5432 - loss: 1.0725

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5431 - loss: 1.0725

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5430 - loss: 1.0725

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5430 - loss: 1.0726

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5429 - loss: 1.0726

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5429 - loss: 1.0726

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5428 - loss: 1.0726

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5428 - loss: 1.0726

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5427 - loss: 1.0726

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5426 - loss: 1.0726

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0726

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0727

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5425 - loss: 1.0727

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5424 - loss: 1.0727

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5424 - loss: 1.0727

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5423 - loss: 1.0727

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5422 - loss: 1.0728

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5422 - loss: 1.0728

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5421 - loss: 1.0728

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5420 - loss: 1.0729

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5419 - loss: 1.0729

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5419 - loss: 1.0729

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5418 - loss: 1.0730

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5417 - loss: 1.0730

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5417 - loss: 1.0730

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5416 - loss: 1.0731

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5416 - loss: 1.0731

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0731

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0731

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5415 - loss: 1.0732

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5414 - loss: 1.0732

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5414 - loss: 1.0732

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0732

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0732

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5413 - loss: 1.0733

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5412 - loss: 1.0733

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5412 - loss: 1.0733

473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5410 - loss: 1.0734
Epoch 10: val_accuracy did not improve from 0.55917

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5410 - loss: 1.0734 - val_accuracy: 0.5166 - val_loss: 1.0749 - learning_rate: 0.0010
Epoch 11/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.5312 - loss: 1.0139

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4980 - loss: 1.0463   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5017 - loss: 1.0491

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5061 - loss: 1.0504

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5109 - loss: 1.0500

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5149 - loss: 1.0492

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5175 - loss: 1.0502

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5192 - loss: 1.0501

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5219 - loss: 1.0502

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5234 - loss: 1.0507

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402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0808

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5294 - loss: 1.0809

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5293 - loss: 1.0810

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0810

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0810

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0811

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5293 - loss: 1.0811

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0811

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0812

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5292 - loss: 1.0813

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0813

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0813

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5291 - loss: 1.0814
Epoch 11: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 11: val_accuracy did not improve from 0.55917

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5291 - loss: 1.0814 - val_accuracy: 0.5477 - val_loss: 1.0631 - learning_rate: 0.0010
Epoch 12/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.6562 - loss: 1.0261

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5788 - loss: 1.0697   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5593 - loss: 1.0767

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5515 - loss: 1.0740

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.0712

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5503 - loss: 1.0649

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5500 - loss: 1.0636

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5496 - loss: 1.0640

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5490 - loss: 1.0657

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5481 - loss: 1.0673

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5481 - loss: 1.0681

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5480 - loss: 1.0680

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5477 - loss: 1.0679

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0674

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0669

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5478 - loss: 1.0659

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5480 - loss: 1.0653

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5479 - loss: 1.0649

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5476 - loss: 1.0647

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5474 - loss: 1.0645

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5471 - loss: 1.0645

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0644

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0641

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5468 - loss: 1.0639

 72/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5468 - loss: 1.0635

 75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5469 - loss: 1.0631

 78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5470 - loss: 1.0626

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5471 - loss: 1.0621

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5472 - loss: 1.0618

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5473 - loss: 1.0614

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5475 - loss: 1.0610

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5477 - loss: 1.0606

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5479 - loss: 1.0601

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5481 - loss: 1.0596

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5484 - loss: 1.0591

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5487 - loss: 1.0586

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5490 - loss: 1.0580

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5493 - loss: 1.0574

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5495 - loss: 1.0569

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5497 - loss: 1.0566

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5498 - loss: 1.0561

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5500 - loss: 1.0557

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5501 - loss: 1.0553

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5502 - loss: 1.0550

130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5503 - loss: 1.0548

133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5504 - loss: 1.0545

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5505 - loss: 1.0543

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5506 - loss: 1.0540

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5507 - loss: 1.0538

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5507 - loss: 1.0537

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5508 - loss: 1.0534

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5509 - loss: 1.0532

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5509 - loss: 1.0531

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5509 - loss: 1.0529

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5510 - loss: 1.0528

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5510 - loss: 1.0527

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5511 - loss: 1.0527

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5511 - loss: 1.0526

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5512 - loss: 1.0525

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5512 - loss: 1.0524

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5513 - loss: 1.0523

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5513 - loss: 1.0522

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0521

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0520

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0520

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0519

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0519

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0518

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0518

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0517

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0516

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5513 - loss: 1.0516

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5512 - loss: 1.0516

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5512 - loss: 1.0515

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5512 - loss: 1.0515

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5512 - loss: 1.0514

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5512 - loss: 1.0513

227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0512

230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0511

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0511

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0510

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0509

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0509

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0508

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0507

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0506

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0506

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5512 - loss: 1.0505

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5511 - loss: 1.0504

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5511 - loss: 1.0504

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5511 - loss: 1.0503

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5511 - loss: 1.0502

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5511 - loss: 1.0502

275/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5511 - loss: 1.0501

278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5511 - loss: 1.0501

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Epoch 12: val_accuracy improved from 0.55917 to 0.57062, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

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Epoch 13/40
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Epoch 13: val_accuracy improved from 0.57062 to 0.57304, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5456 - loss: 1.0510 - val_accuracy: 0.5730 - val_loss: 0.9909 - learning_rate: 2.0000e-04
Epoch 14/40
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414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0405

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0404

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0404

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0403

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5542 - loss: 1.0403

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5542 - loss: 1.0402

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0402

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0401

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0401

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0400

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0400

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0399

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0399

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0399

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0398

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0398

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0398

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0397

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0397
Epoch 14: val_accuracy did not improve from 0.57304

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5547 - loss: 1.0396 - val_accuracy: 0.5640 - val_loss: 1.0161 - learning_rate: 2.0000e-04
Epoch 15/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.5000 - loss: 1.1099

  4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5319 - loss: 1.0903  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.5346 - loss: 1.0911

 10/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5363 - loss: 1.0867

 13/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5359 - loss: 1.0808

 16/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5349 - loss: 1.0785

 19/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5349 - loss: 1.0752

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5351 - loss: 1.0725 

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5355 - loss: 1.0700

 28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5361 - loss: 1.0683

 31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5366 - loss: 1.0664

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5374 - loss: 1.0643

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5383 - loss: 1.0620

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5391 - loss: 1.0599

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5398 - loss: 1.0580

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5407 - loss: 1.0563

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5416 - loss: 1.0545

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5424 - loss: 1.0531

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5430 - loss: 1.0521

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5434 - loss: 1.0514

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5436 - loss: 1.0508

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5438 - loss: 1.0503

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0501

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0498

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0496

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0491

 78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0489

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5440 - loss: 1.0487

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5441 - loss: 1.0484

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5443 - loss: 1.0480

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5446 - loss: 1.0476

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5448 - loss: 1.0472

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5451 - loss: 1.0469

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5454 - loss: 1.0465

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5456 - loss: 1.0461

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5458 - loss: 1.0457

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5460 - loss: 1.0454

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5462 - loss: 1.0451

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5464 - loss: 1.0447

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5465 - loss: 1.0444

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5467 - loss: 1.0441

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5468 - loss: 1.0437

126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5469 - loss: 1.0435

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5470 - loss: 1.0433

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5471 - loss: 1.0431

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5472 - loss: 1.0430

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5472 - loss: 1.0429

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5473 - loss: 1.0427

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5475 - loss: 1.0425

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5476 - loss: 1.0424

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5476 - loss: 1.0423

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5477 - loss: 1.0421

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5478 - loss: 1.0421

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5479 - loss: 1.0420

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5479 - loss: 1.0419

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5480 - loss: 1.0418

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5481 - loss: 1.0417

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5481 - loss: 1.0417

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5482 - loss: 1.0416

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5482 - loss: 1.0415

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5483 - loss: 1.0415

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5484 - loss: 1.0414

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5484 - loss: 1.0414

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5485 - loss: 1.0414

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5485 - loss: 1.0414

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5485 - loss: 1.0414

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5486 - loss: 1.0414

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5486 - loss: 1.0414

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5486 - loss: 1.0415

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5487 - loss: 1.0415

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5487 - loss: 1.0415

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5487 - loss: 1.0415

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5487 - loss: 1.0415

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5487 - loss: 1.0415

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5488 - loss: 1.0414

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5488 - loss: 1.0414

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5489 - loss: 1.0414

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5489 - loss: 1.0413

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5489 - loss: 1.0413

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5490 - loss: 1.0413

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5490 - loss: 1.0413

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5490 - loss: 1.0413

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5491 - loss: 1.0412

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5492 - loss: 1.0412

279/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5492 - loss: 1.0412

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0411

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0411

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0412

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0413

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0413

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0413

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0413

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5492 - loss: 1.0413

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0414

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5491 - loss: 1.0415

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

372/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

375/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5490 - loss: 1.0415

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5490 - loss: 1.0415

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5490 - loss: 1.0415

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5490 - loss: 1.0415

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5490 - loss: 1.0415

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5490 - loss: 1.0415

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5491 - loss: 1.0415

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5492 - loss: 1.0414

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5492 - loss: 1.0414

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5492 - loss: 1.0414

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5492 - loss: 1.0414

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5492 - loss: 1.0414

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5493 - loss: 1.0413

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5493 - loss: 1.0413

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5493 - loss: 1.0413

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5493 - loss: 1.0413

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5493 - loss: 1.0412

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0412

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5494 - loss: 1.0411

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5495 - loss: 1.0411

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5495 - loss: 1.0411
Epoch 15: val_accuracy did not improve from 0.57304

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5495 - loss: 1.0410 - val_accuracy: 0.5674 - val_loss: 1.0008 - learning_rate: 2.0000e-04
Epoch 16/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.5000 - loss: 1.0865

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5651 - loss: 1.0210   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5681 - loss: 1.0212

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5650 - loss: 1.0254

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5646 - loss: 1.0254

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5648 - loss: 1.0246

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5660 - loss: 1.0235

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5674 - loss: 1.0219

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0202

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5677 - loss: 1.0188

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5673 - loss: 1.0177

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5668 - loss: 1.0166

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5663 - loss: 1.0160

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5658 - loss: 1.0159

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5649 - loss: 1.0166

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5643 - loss: 1.0170

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5637 - loss: 1.0176

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5633 - loss: 1.0180

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5630 - loss: 1.0186

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5629 - loss: 1.0188

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5630 - loss: 1.0189

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5630 - loss: 1.0189

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5632 - loss: 1.0188

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5633 - loss: 1.0186

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5635 - loss: 1.0185

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5636 - loss: 1.0183

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5637 - loss: 1.0182

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5638 - loss: 1.0182

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5640 - loss: 1.0181

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5642 - loss: 1.0179

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5643 - loss: 1.0179

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5645 - loss: 1.0177

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5646 - loss: 1.0176

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5648 - loss: 1.0174

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5650 - loss: 1.0172

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5652 - loss: 1.0170

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5655 - loss: 1.0168

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5656 - loss: 1.0167

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5658 - loss: 1.0166

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5660 - loss: 1.0165

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5661 - loss: 1.0165

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5662 - loss: 1.0164

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5664 - loss: 1.0164

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5664 - loss: 1.0164

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5665 - loss: 1.0164

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5666 - loss: 1.0164

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5666 - loss: 1.0164

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5667 - loss: 1.0165

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0165

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0165

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0166

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0166

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0167

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0168

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0169

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5665 - loss: 1.0171

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0172

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0174

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0176

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0179

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0182

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0183

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5657 - loss: 1.0185

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5656 - loss: 1.0187

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5655 - loss: 1.0190

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5654 - loss: 1.0192

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5653 - loss: 1.0195

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0197

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5651 - loss: 1.0199

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5650 - loss: 1.0201

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5649 - loss: 1.0203

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5648 - loss: 1.0204

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5647 - loss: 1.0206

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5646 - loss: 1.0207

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5645 - loss: 1.0209

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0210

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5643 - loss: 1.0211

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5643 - loss: 1.0212

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5642 - loss: 1.0214

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5641 - loss: 1.0215

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5639 - loss: 1.0217

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5639 - loss: 1.0218

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5638 - loss: 1.0220

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0221

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0222

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0223

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0224

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0225

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0226

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0227

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0228

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5632 - loss: 1.0229

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5631 - loss: 1.0230

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5631 - loss: 1.0231

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5630 - loss: 1.0231

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5630 - loss: 1.0232

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5629 - loss: 1.0233

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5629 - loss: 1.0234

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5628 - loss: 1.0234

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0235

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0236

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0236

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0237

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0238

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0238

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0239

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5625 - loss: 1.0239

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5624 - loss: 1.0240

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5623 - loss: 1.0241

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5622 - loss: 1.0242

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0242

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5622 - loss: 1.0243

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0243

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5621 - loss: 1.0244

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0244

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5620 - loss: 1.0245

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5620 - loss: 1.0245

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0245

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5619 - loss: 1.0246

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0246

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5618 - loss: 1.0247

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5617 - loss: 1.0247

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5617 - loss: 1.0247

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5617 - loss: 1.0247

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5617 - loss: 1.0247

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0247

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5616 - loss: 1.0248

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0248

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0249

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5615 - loss: 1.0249

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0249

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0250

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5614 - loss: 1.0250

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5613 - loss: 1.0251

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5612 - loss: 1.0252
Epoch 16: val_accuracy did not improve from 0.57304

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5612 - loss: 1.0252 - val_accuracy: 0.5322 - val_loss: 1.0739 - learning_rate: 2.0000e-04
Epoch 17/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.4688 - loss: 1.1418

  3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5087 - loss: 1.0576  

  6/473 ━━━━━━━━━━━━━━━━━━━━ 12s 28ms/step - accuracy: 0.5242 - loss: 1.0388

  9/473 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.5362 - loss: 1.0323

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5429 - loss: 1.0319

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5483 - loss: 1.0293

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5535 - loss: 1.0253 

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5568 - loss: 1.0213

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5583 - loss: 1.0193

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5598 - loss: 1.0178

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5611 - loss: 1.0175

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5619 - loss: 1.0177

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5619 - loss: 1.0191

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5616 - loss: 1.0207

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5609 - loss: 1.0224

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5606 - loss: 1.0235

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5605 - loss: 1.0242

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5603 - loss: 1.0249

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5602 - loss: 1.0253

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5600 - loss: 1.0258

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423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5574 - loss: 1.0323

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0323

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0323

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5574 - loss: 1.0324

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0324

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0324

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0325

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0326

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5573 - loss: 1.0326

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5572 - loss: 1.0326
Epoch 17: val_accuracy did not improve from 0.57304

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5572 - loss: 1.0327 - val_accuracy: 0.5716 - val_loss: 0.9860 - learning_rate: 2.0000e-04
Epoch 18/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6562 - loss: 0.8536

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6204 - loss: 0.9114   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6065 - loss: 0.9348

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5962 - loss: 0.9524

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5882 - loss: 0.9677

 17/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5815 - loss: 0.9785

 20/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5772 - loss: 0.9870

 23/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5732 - loss: 0.9935

 26/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5696 - loss: 0.9981

 29/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0017

 32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5661 - loss: 1.0044

 35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5655 - loss: 1.0063

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5651 - loss: 1.0080

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5645 - loss: 1.0100

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0112

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5640 - loss: 1.0119

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0122

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5642 - loss: 1.0125

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5642 - loss: 1.0128

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5641 - loss: 1.0130

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5640 - loss: 1.0132

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5639 - loss: 1.0134

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5638 - loss: 1.0137

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5636 - loss: 1.0142

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5633 - loss: 1.0147

 75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0150

 78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0152

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0154

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0157

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0159

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0161

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0162

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0164

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5634 - loss: 1.0166

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0168

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0170

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5632 - loss: 1.0172

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5631 - loss: 1.0174

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5631 - loss: 1.0176

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5629 - loss: 1.0178

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5628 - loss: 1.0181

123/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5627 - loss: 1.0183

126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5626 - loss: 1.0185

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5625 - loss: 1.0188

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5624 - loss: 1.0190

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5623 - loss: 1.0191

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5622 - loss: 1.0192

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5622 - loss: 1.0194

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0195

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0196

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0197

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0197

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0198

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0199

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0200

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0201

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0202

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5619 - loss: 1.0204

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5619 - loss: 1.0205

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5619 - loss: 1.0206

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5619 - loss: 1.0207

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0208

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0209

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0210

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0211

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0212

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0212

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0213

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0214

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0215

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0215

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0216

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0216

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0217

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0217

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0218

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0218

227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0219

230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0219

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0220

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0220

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0220

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0221

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0221

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0221

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0222

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0222

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0223

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0223

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5618 - loss: 1.0224

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5617 - loss: 1.0225

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5617 - loss: 1.0226

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5617 - loss: 1.0227

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5616 - loss: 1.0227

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5616 - loss: 1.0228

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5616 - loss: 1.0229

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5616 - loss: 1.0230

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5616 - loss: 1.0230

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5616 - loss: 1.0231

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5615 - loss: 1.0232

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5615 - loss: 1.0232

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5615 - loss: 1.0233

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5614 - loss: 1.0234

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5614 - loss: 1.0234

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5614 - loss: 1.0235

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Epoch 18: val_accuracy improved from 0.57304 to 0.57746, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5604 - loss: 1.0255 - val_accuracy: 0.5775 - val_loss: 0.9880 - learning_rate: 2.0000e-04
Epoch 19/40
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Epoch 19: val_accuracy improved from 0.57746 to 0.58188, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5605 - loss: 1.0270 - val_accuracy: 0.5819 - val_loss: 0.9830 - learning_rate: 2.0000e-04
Epoch 20/40
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Epoch 20: val_accuracy improved from 0.58188 to 0.58268, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

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Epoch 21/40
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307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5706 - loss: 1.0195

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5705 - loss: 1.0196

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5705 - loss: 1.0196

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5704 - loss: 1.0197

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5704 - loss: 1.0197

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5703 - loss: 1.0198

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5702 - loss: 1.0198

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5702 - loss: 1.0198

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5701 - loss: 1.0199

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5701 - loss: 1.0199

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5700 - loss: 1.0199

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5699 - loss: 1.0200

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5699 - loss: 1.0200

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5699 - loss: 1.0200

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5698 - loss: 1.0200

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5698 - loss: 1.0200

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5698 - loss: 1.0200

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5697 - loss: 1.0200

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5697 - loss: 1.0200

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5697 - loss: 1.0200

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5696 - loss: 1.0200

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5696 - loss: 1.0201

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5695 - loss: 1.0201

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5695 - loss: 1.0201

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5695 - loss: 1.0201

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5694 - loss: 1.0201

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5694 - loss: 1.0201

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5693 - loss: 1.0201

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5693 - loss: 1.0202

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5693 - loss: 1.0202

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5692 - loss: 1.0202

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5692 - loss: 1.0203

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5692 - loss: 1.0203

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5691 - loss: 1.0203

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5691 - loss: 1.0203

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5690 - loss: 1.0203

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5690 - loss: 1.0204

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5689 - loss: 1.0204

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5689 - loss: 1.0205

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0205

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5688 - loss: 1.0205

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5688 - loss: 1.0205

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5687 - loss: 1.0206

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5687 - loss: 1.0206

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5686 - loss: 1.0206

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5686 - loss: 1.0206

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5686 - loss: 1.0206

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5685 - loss: 1.0207

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5685 - loss: 1.0207

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5685 - loss: 1.0207

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0207

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0207

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0207

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5683 - loss: 1.0207

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5683 - loss: 1.0208
Epoch 21: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5682 - loss: 1.0208 - val_accuracy: 0.5799 - val_loss: 0.9848 - learning_rate: 2.0000e-04
Epoch 22/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.5312 - loss: 1.0835

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5703 - loss: 1.0478   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5619 - loss: 1.0701

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5530 - loss: 1.0808

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5516 - loss: 1.0795

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5491 - loss: 1.0765

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5477 - loss: 1.0722

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5471 - loss: 1.0680

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5470 - loss: 1.0640

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5475 - loss: 1.0592

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5485 - loss: 1.0547

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5496 - loss: 1.0506

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5509 - loss: 1.0467

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5522 - loss: 1.0433

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5533 - loss: 1.0409

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5544 - loss: 1.0388

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5550 - loss: 1.0376

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5555 - loss: 1.0364

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5565 - loss: 1.0347

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5573 - loss: 1.0333

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5582 - loss: 1.0319

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5590 - loss: 1.0307

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5598 - loss: 1.0297

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5605 - loss: 1.0289

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5611 - loss: 1.0281

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5615 - loss: 1.0275

 77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5620 - loss: 1.0269

 80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5624 - loss: 1.0262

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5628 - loss: 1.0255

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5631 - loss: 1.0250

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5633 - loss: 1.0246

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5635 - loss: 1.0242

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5636 - loss: 1.0239

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5638 - loss: 1.0236

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0232

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0229

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0226

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0224

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5640 - loss: 1.0221

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0219

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5639 - loss: 1.0217

123/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0215

126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0213

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0211

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0210

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0209

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0209

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0208

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0206

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0207

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0207

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0206

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5639 - loss: 1.0205

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5640 - loss: 1.0204

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5641 - loss: 1.0203

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5642 - loss: 1.0202

173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0201

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0200

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0199

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0199

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0199

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0198

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0198

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0197

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0197

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0197

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0198

226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0199

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0199

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0200

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0201

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0201

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5647 - loss: 1.0202

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0203

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0203

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0204

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0205

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0206

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5646 - loss: 1.0207

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0208

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0208

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0209

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5645 - loss: 1.0210

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5644 - loss: 1.0211

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0212

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0212

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0213

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0214

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5644 - loss: 1.0215

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0215

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0216

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0217

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0218

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0218

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0219

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5643 - loss: 1.0219

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0220

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0221

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0221

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0222

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5642 - loss: 1.0222

326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0222

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5642 - loss: 1.0223

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0224

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0225

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5641 - loss: 1.0226

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5640 - loss: 1.0227

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0227

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5640 - loss: 1.0228

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0229

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0230

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0230

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0230

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0231

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5638 - loss: 1.0232

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0232

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0233

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5637 - loss: 1.0234
Epoch 22: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5636 - loss: 1.0234 - val_accuracy: 0.5650 - val_loss: 1.0085 - learning_rate: 2.0000e-04
Epoch 23/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.6875 - loss: 0.8717

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6016 - loss: 0.9734   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5868 - loss: 1.0083

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5851 - loss: 1.0162

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5848 - loss: 1.0141

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5866 - loss: 1.0092

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5856 - loss: 1.0094

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5861 - loss: 1.0079

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5861 - loss: 1.0064

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5858 - loss: 1.0052

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5845 - loss: 1.0050

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5831 - loss: 1.0051

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5818 - loss: 1.0051

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5810 - loss: 1.0051

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5807 - loss: 1.0047

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5802 - loss: 1.0048

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5798 - loss: 1.0046

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5794 - loss: 1.0042

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5793 - loss: 1.0036

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5792 - loss: 1.0030

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5792 - loss: 1.0026

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5791 - loss: 1.0024

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5789 - loss: 1.0020

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5786 - loss: 1.0018

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5785 - loss: 1.0016

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5786 - loss: 1.0013

 77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5786 - loss: 1.0010

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432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0175

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0176

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0176

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0177

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5678 - loss: 1.0178

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0178

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0178

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0179

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5677 - loss: 1.0179
Epoch 23: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5677 - loss: 1.0179 - val_accuracy: 0.5734 - val_loss: 0.9839 - learning_rate: 2.0000e-04
Epoch 24/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5625 - loss: 0.9864

  3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5729 - loss: 0.9790  

  5/473 ━━━━━━━━━━━━━━━━━━━━ 17s 37ms/step - accuracy: 0.5884 - loss: 0.9422

  7/473 ━━━━━━━━━━━━━━━━━━━━ 15s 34ms/step - accuracy: 0.5941 - loss: 0.9322

 10/473 ━━━━━━━━━━━━━━━━━━━━ 13s 29ms/step - accuracy: 0.5942 - loss: 0.9308

 13/473 ━━━━━━━━━━━━━━━━━━━━ 12s 27ms/step - accuracy: 0.5926 - loss: 0.9332

 15/473 ━━━━━━━━━━━━━━━━━━━━ 12s 26ms/step - accuracy: 0.5934 - loss: 0.9320

 18/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5929 - loss: 0.9339

 21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5921 - loss: 0.9386

 24/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5910 - loss: 0.9423

 27/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5901 - loss: 0.9448

 30/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5886 - loss: 0.9495

 33/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5872 - loss: 0.9549

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5858 - loss: 0.9597 

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5844 - loss: 0.9644

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5834 - loss: 0.9678

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5830 - loss: 0.9696

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5825 - loss: 0.9719

 50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5820 - loss: 0.9740

 53/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5815 - loss: 0.9759

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5812 - loss: 0.9770

 57/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5810 - loss: 0.9781

 60/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5807 - loss: 0.9795

 63/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5804 - loss: 0.9806

 66/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5799 - loss: 0.9818

 69/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5795 - loss: 0.9830

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5791 - loss: 0.9840

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5788 - loss: 0.9850

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5786 - loss: 0.9856

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5785 - loss: 0.9863

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5783 - loss: 0.9870

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5781 - loss: 0.9877

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5779 - loss: 0.9883

 93/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5776 - loss: 0.9891

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5773 - loss: 0.9899

 99/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 0.9906

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5768 - loss: 0.9914

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9922

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5764 - loss: 0.9927

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5763 - loss: 0.9932

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5761 - loss: 0.9937

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5759 - loss: 0.9943

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5758 - loss: 0.9949

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5756 - loss: 0.9955

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5754 - loss: 0.9960

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5752 - loss: 0.9965

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5750 - loss: 0.9970

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5748 - loss: 0.9974

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5746 - loss: 0.9979

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5744 - loss: 0.9984

141/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5741 - loss: 0.9989

143/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5740 - loss: 0.9992

146/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5737 - loss: 0.9996

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5735 - loss: 1.0000

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5733 - loss: 1.0004

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5732 - loss: 1.0007

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5730 - loss: 1.0009

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5729 - loss: 1.0013

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5727 - loss: 1.0016

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5725 - loss: 1.0020

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5724 - loss: 1.0023

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5723 - loss: 1.0026

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5721 - loss: 1.0029

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5719 - loss: 1.0032

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5718 - loss: 1.0035

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5716 - loss: 1.0038

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5714 - loss: 1.0041

187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5712 - loss: 1.0044

190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5710 - loss: 1.0048

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5709 - loss: 1.0050

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5707 - loss: 1.0053

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5706 - loss: 1.0055

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5705 - loss: 1.0058

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5704 - loss: 1.0060

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5704 - loss: 1.0062

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5703 - loss: 1.0064

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5702 - loss: 1.0066

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5701 - loss: 1.0068

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5700 - loss: 1.0070

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5699 - loss: 1.0072

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5699 - loss: 1.0074

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5698 - loss: 1.0075

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5697 - loss: 1.0076

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5697 - loss: 1.0078

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5696 - loss: 1.0079

239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5695 - loss: 1.0081

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5694 - loss: 1.0083

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5693 - loss: 1.0084

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5693 - loss: 1.0085

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5692 - loss: 1.0087

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5691 - loss: 1.0088

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5691 - loss: 1.0089

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5690 - loss: 1.0091

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5689 - loss: 1.0092

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5689 - loss: 1.0093

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5688 - loss: 1.0094

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5688 - loss: 1.0095

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5687 - loss: 1.0097

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5687 - loss: 1.0098

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5686 - loss: 1.0099

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5686 - loss: 1.0100

283/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5686 - loss: 1.0101

285/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5686 - loss: 1.0102

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5685 - loss: 1.0103

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5685 - loss: 1.0104

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5685 - loss: 1.0105

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5685 - loss: 1.0105

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5685 - loss: 1.0106

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5684 - loss: 1.0107

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5684 - loss: 1.0108

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5684 - loss: 1.0109

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5684 - loss: 1.0110

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5684 - loss: 1.0110

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5683 - loss: 1.0111

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5683 - loss: 1.0111

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5683 - loss: 1.0112

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 22ms/step - accuracy: 0.5683 - loss: 1.0113

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0113

331/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5683 - loss: 1.0114

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0114

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0115

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0115

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0115

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0115

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0116

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0116

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0116

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - accuracy: 0.5683 - loss: 1.0117

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0117

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0118

379/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0118

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0118

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0118

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0118

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0119

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0119

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0119

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0119

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0119

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0120

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0120

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0120

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0120

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0121

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0121

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0121

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5684 - loss: 1.0121

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0121

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0121

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0122

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0122

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0122

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0122

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0123

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5684 - loss: 1.0124
Epoch 24: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 24: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5684 - loss: 1.0124 - val_accuracy: 0.5728 - val_loss: 0.9909 - learning_rate: 2.0000e-04
Epoch 25/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 173ms/step - accuracy: 0.6250 - loss: 0.9017

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5990 - loss: 0.9397   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5865 - loss: 0.9619

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5829 - loss: 0.9735

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5800 - loss: 0.9795

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5760 - loss: 0.9853

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5746 - loss: 0.9871

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5723 - loss: 0.9905

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5701 - loss: 0.9933

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5691 - loss: 0.9948

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5682 - loss: 0.9967

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5676 - loss: 0.9984

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5675 - loss: 0.9990

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5673 - loss: 0.9997

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5673 - loss: 0.9999

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5673 - loss: 0.9999

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0000

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0002

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5672 - loss: 1.0007

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5674 - loss: 1.0008

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5675 - loss: 1.0011

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5676 - loss: 1.0014

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0016

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0021

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0026

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0031

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0034

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0039

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0042

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0045

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0047

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0048

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0052

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0055

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0057

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0060

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0062

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0063

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0062

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0062

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5678 - loss: 1.0062

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0063

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0064

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5679 - loss: 1.0065

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5678 - loss: 1.0067

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0069

133/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5677 - loss: 1.0071

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5676 - loss: 1.0072

139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5675 - loss: 1.0074

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5674 - loss: 1.0077

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5673 - loss: 1.0079

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5672 - loss: 1.0080

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5671 - loss: 1.0083

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5669 - loss: 1.0085

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5667 - loss: 1.0088

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5666 - loss: 1.0090

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5664 - loss: 1.0092

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5663 - loss: 1.0094

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5662 - loss: 1.0096

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5661 - loss: 1.0097

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5660 - loss: 1.0099

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5659 - loss: 1.0100

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5658 - loss: 1.0101

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5657 - loss: 1.0103

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5656 - loss: 1.0104

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5655 - loss: 1.0105

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5655 - loss: 1.0106

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5654 - loss: 1.0107

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5653 - loss: 1.0108

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0110

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5652 - loss: 1.0111

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5651 - loss: 1.0113

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5650 - loss: 1.0114

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5649 - loss: 1.0115

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5648 - loss: 1.0116

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5647 - loss: 1.0117

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5646 - loss: 1.0118

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5645 - loss: 1.0120

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0120

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5644 - loss: 1.0121

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5643 - loss: 1.0122

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5642 - loss: 1.0123

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5641 - loss: 1.0124

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5641 - loss: 1.0124

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5640 - loss: 1.0125

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5640 - loss: 1.0125

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5639 - loss: 1.0126

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5639 - loss: 1.0127

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5638 - loss: 1.0128

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0128

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5637 - loss: 1.0129

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5636 - loss: 1.0130

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0130

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5635 - loss: 1.0131

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0131

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5634 - loss: 1.0132

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0132

280/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5633 - loss: 1.0133

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5632 - loss: 1.0133

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5632 - loss: 1.0134

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5631 - loss: 1.0134

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5631 - loss: 1.0135

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5630 - loss: 1.0135

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5630 - loss: 1.0135

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5629 - loss: 1.0136

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5629 - loss: 1.0136

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0137

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5628 - loss: 1.0137

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0137

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5627 - loss: 1.0138

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0138

328/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5626 - loss: 1.0138

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0138

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5626 - loss: 1.0138

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5625 - loss: 1.0139

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0139

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5624 - loss: 1.0138

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0138

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5624 - loss: 1.0137

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5624 - loss: 1.0137
Epoch 25: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5624 - loss: 1.0136 - val_accuracy: 0.5690 - val_loss: 0.9963 - learning_rate: 4.0000e-05
Epoch 26/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:21 172ms/step - accuracy: 0.5312 - loss: 0.9227

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6022 - loss: 0.9168   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5856 - loss: 0.9609

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5733 - loss: 0.9828

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5675 - loss: 0.9946

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5657 - loss: 1.0002

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5654 - loss: 1.0031

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5664 - loss: 1.0047

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5666 - loss: 1.0072

 29/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0080

 32/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5682 - loss: 1.0081

 35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5690 - loss: 1.0077

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5700 - loss: 1.0070

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5710 - loss: 1.0063

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5717 - loss: 1.0056

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5720 - loss: 1.0054

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5724 - loss: 1.0051

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5728 - loss: 1.0047

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5732 - loss: 1.0043

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5737 - loss: 1.0037

 62/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5743 - loss: 1.0030

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428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5772 - loss: 0.9952

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5772 - loss: 0.9952

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9953

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9953

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9954

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5771 - loss: 0.9954

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9955

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9955

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5770 - loss: 0.9956

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9956

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9956

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9957

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5769 - loss: 0.9957

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9958

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5768 - loss: 0.9959
Epoch 26: val_accuracy did not improve from 0.58268

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5767 - loss: 0.9960 - val_accuracy: 0.5698 - val_loss: 1.0093 - learning_rate: 4.0000e-05
Epoch 27/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.4688 - loss: 1.1381

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5371 - loss: 1.0224   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5443 - loss: 1.0022

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5417 - loss: 1.0114

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5366 - loss: 1.0244

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5356 - loss: 1.0284

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5365 - loss: 1.0288

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5389 - loss: 1.0270

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5409 - loss: 1.0263

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5426 - loss: 1.0268

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5432 - loss: 1.0281

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5438 - loss: 1.0293

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5444 - loss: 1.0296

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5448 - loss: 1.0299

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5448 - loss: 1.0307

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5447 - loss: 1.0313

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0322

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5442 - loss: 1.0330

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0336

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5439 - loss: 1.0340

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0342

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5439 - loss: 1.0345

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5441 - loss: 1.0345

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5443 - loss: 1.0344

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5444 - loss: 1.0345

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0345

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5447 - loss: 1.0345

 80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5448 - loss: 1.0345

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0345

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0346

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5449 - loss: 1.0347

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5450 - loss: 1.0347

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5450 - loss: 1.0347

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5451 - loss: 1.0347

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5452 - loss: 1.0347

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5452 - loss: 1.0346

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5452 - loss: 1.0346

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5453 - loss: 1.0345

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5454 - loss: 1.0344

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5454 - loss: 1.0343

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5455 - loss: 1.0342

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5456 - loss: 1.0341

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5456 - loss: 1.0341

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5457 - loss: 1.0341

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5458 - loss: 1.0340

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5459 - loss: 1.0338

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5461 - loss: 1.0337

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5462 - loss: 1.0335

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5464 - loss: 1.0333

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5465 - loss: 1.0332

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5467 - loss: 1.0330

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5468 - loss: 1.0328

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5469 - loss: 1.0326

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5471 - loss: 1.0324

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5473 - loss: 1.0321

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5475 - loss: 1.0319

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5476 - loss: 1.0317

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5478 - loss: 1.0316

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5479 - loss: 1.0314

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5481 - loss: 1.0312

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5483 - loss: 1.0310

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5486 - loss: 1.0308

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5488 - loss: 1.0306

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5490 - loss: 1.0304

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5492 - loss: 1.0302

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5494 - loss: 1.0300

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5497 - loss: 1.0299

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5498 - loss: 1.0298

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5500 - loss: 1.0297

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5502 - loss: 1.0296

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5503 - loss: 1.0295

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5505 - loss: 1.0293

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5506 - loss: 1.0292

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5508 - loss: 1.0291

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5509 - loss: 1.0290

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5510 - loss: 1.0289

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5511 - loss: 1.0288

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5512 - loss: 1.0287

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5513 - loss: 1.0286

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5515 - loss: 1.0285

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5516 - loss: 1.0284

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5517 - loss: 1.0283

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5518 - loss: 1.0282

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5519 - loss: 1.0281

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5521 - loss: 1.0279

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5522 - loss: 1.0278

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5523 - loss: 1.0276

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5525 - loss: 1.0275

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5526 - loss: 1.0273

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5527 - loss: 1.0272

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5528 - loss: 1.0271

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5529 - loss: 1.0270

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5530 - loss: 1.0269

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5531 - loss: 1.0268

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5532 - loss: 1.0267

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5533 - loss: 1.0266

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5534 - loss: 1.0266

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5534 - loss: 1.0265

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5535 - loss: 1.0264

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5535 - loss: 1.0263

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5536 - loss: 1.0262

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5536 - loss: 1.0262

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5537 - loss: 1.0261

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5537 - loss: 1.0260

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5538 - loss: 1.0259

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Epoch 27: val_accuracy improved from 0.58268 to 0.58348, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_resnet_20240411-002041.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5577 - loss: 1.0212 - val_accuracy: 0.5835 - val_loss: 0.9727 - learning_rate: 4.0000e-05
Epoch 28/40
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187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5800 - loss: 0.9924

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5799 - loss: 0.9925

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5798 - loss: 0.9927

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9928

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9929

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5796 - loss: 0.9930

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5795 - loss: 0.9932

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5795 - loss: 0.9933

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5794 - loss: 0.9934

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5793 - loss: 0.9936

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5793 - loss: 0.9937

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5792 - loss: 0.9938

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5792 - loss: 0.9939

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5792 - loss: 0.9940

229/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5791 - loss: 0.9941

232/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5791 - loss: 0.9942

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5790 - loss: 0.9943

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9944

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 0.9945

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5788 - loss: 0.9946

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5788 - loss: 0.9947

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5787 - loss: 0.9947

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5787 - loss: 0.9948

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9948

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9949

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9950

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9950

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9951

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9952

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 0.9952

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9953

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9954

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9955

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5781 - loss: 0.9955

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5780 - loss: 0.9956

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5780 - loss: 0.9956

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9957

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9957

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 0.9958

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9958

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9959

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9960

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9960

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9961

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9961

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9962

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9962

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9963

327/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 0.9963

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5773 - loss: 0.9964

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5772 - loss: 0.9964

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5772 - loss: 0.9965

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5772 - loss: 0.9965

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9966

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9966

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9967

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9968

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9968

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9969

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9970

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9970

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9971

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9971

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9972

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9972

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9973

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9974

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9974

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9975

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9975

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9976

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9976

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9977

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9978

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9978

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9979

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9979

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9980

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9980

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9981

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9981

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5760 - loss: 0.9982

425/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5759 - loss: 0.9983

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9983

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9983

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9984

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9984

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9985

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9986

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9987

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9987

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9988

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9988
Epoch 28: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5755 - loss: 0.9989 - val_accuracy: 0.5781 - val_loss: 0.9748 - learning_rate: 4.0000e-05
Epoch 29/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5625 - loss: 1.1707

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5339 - loss: 1.1834   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5447 - loss: 1.1443

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5492 - loss: 1.1234

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.1124

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5476 - loss: 1.1036

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5473 - loss: 1.0949

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0890

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0855

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0832

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5440 - loss: 1.0817

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5436 - loss: 1.0805

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5439 - loss: 1.0777

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5445 - loss: 1.0746

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5451 - loss: 1.0717

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5458 - loss: 1.0689

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5462 - loss: 1.0672

 48/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5465 - loss: 1.0657

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5470 - loss: 1.0635

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5477 - loss: 1.0612

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5482 - loss: 1.0593

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5486 - loss: 1.0575

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5490 - loss: 1.0560

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427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0192

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5632 - loss: 1.0191

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0191

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0191

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0190

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0189

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5633 - loss: 1.0189

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0189

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0188

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5634 - loss: 1.0188
Epoch 29: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5634 - loss: 1.0187 - val_accuracy: 0.5752 - val_loss: 0.9973 - learning_rate: 4.0000e-05
Epoch 30/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6250 - loss: 0.9807

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6510 - loss: 0.8811   

  6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.6323 - loss: 0.8955

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6188 - loss: 0.9165

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.6101 - loss: 0.9329

 15/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.6098 - loss: 0.9382

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6100 - loss: 0.9412

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.6075 - loss: 0.9457 

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6043 - loss: 0.9513

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6015 - loss: 0.9564

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5984 - loss: 0.9615

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5968 - loss: 0.9639

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9662

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5949 - loss: 0.9677

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5941 - loss: 0.9691

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5931 - loss: 0.9706

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5923 - loss: 0.9721

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5918 - loss: 0.9731

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5913 - loss: 0.9742

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5910 - loss: 0.9750

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5907 - loss: 0.9758

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5904 - loss: 0.9767

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5902 - loss: 0.9773

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5900 - loss: 0.9779

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5897 - loss: 0.9788

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5895 - loss: 0.9795

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5894 - loss: 0.9801

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5892 - loss: 0.9808

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5889 - loss: 0.9816

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5886 - loss: 0.9824

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5882 - loss: 0.9831

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5879 - loss: 0.9838

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5876 - loss: 0.9844

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5873 - loss: 0.9850

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5871 - loss: 0.9857

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5869 - loss: 0.9861

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5866 - loss: 0.9867

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5864 - loss: 0.9872

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5861 - loss: 0.9878

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5859 - loss: 0.9883

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5856 - loss: 0.9888

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5854 - loss: 0.9893

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5852 - loss: 0.9896

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5850 - loss: 0.9901

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5848 - loss: 0.9905

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5847 - loss: 0.9908

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5845 - loss: 0.9911

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5844 - loss: 0.9913

139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5843 - loss: 0.9916

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5841 - loss: 0.9920

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 0.9924

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5839 - loss: 0.9926

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5837 - loss: 0.9929

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5836 - loss: 0.9932

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5835 - loss: 0.9935

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5833 - loss: 0.9937

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5832 - loss: 0.9940

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5830 - loss: 0.9943

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5828 - loss: 0.9947

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5826 - loss: 0.9950

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5824 - loss: 0.9954

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5822 - loss: 0.9957

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5820 - loss: 0.9961

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5818 - loss: 0.9964

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5815 - loss: 0.9968

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5813 - loss: 0.9971

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5812 - loss: 0.9974

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5810 - loss: 0.9976

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5809 - loss: 0.9978

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5808 - loss: 0.9981

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5806 - loss: 0.9983

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5805 - loss: 0.9985

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5805 - loss: 0.9986

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5804 - loss: 0.9988

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5803 - loss: 0.9989

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5802 - loss: 0.9991

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5801 - loss: 0.9992

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5800 - loss: 0.9993

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5799 - loss: 0.9995

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5798 - loss: 0.9997

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5797 - loss: 0.9998

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5796 - loss: 1.0000

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5795 - loss: 1.0001

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5794 - loss: 1.0002

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5793 - loss: 1.0004

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5792 - loss: 1.0005

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5791 - loss: 1.0007

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5790 - loss: 1.0008

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5789 - loss: 1.0010

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5788 - loss: 1.0011

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5787 - loss: 1.0012

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 1.0014

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 1.0015

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 1.0016

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 1.0017

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 1.0019

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 1.0020

280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5780 - loss: 1.0021

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5780 - loss: 1.0022

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 1.0023

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 1.0024

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 1.0025

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 1.0026

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 1.0027

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 1.0028

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 1.0029

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 1.0030

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5772 - loss: 1.0031

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5771 - loss: 1.0032

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5771 - loss: 1.0033

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5770 - loss: 1.0034

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5769 - loss: 1.0034

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5768 - loss: 1.0035

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5767 - loss: 1.0036

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5766 - loss: 1.0037

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 1.0038

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5765 - loss: 1.0038

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5765 - loss: 1.0039

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5764 - loss: 1.0040

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5763 - loss: 1.0041

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5762 - loss: 1.0042

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5761 - loss: 1.0043

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5761 - loss: 1.0043

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5760 - loss: 1.0044

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5759 - loss: 1.0045

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5758 - loss: 1.0045

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5758 - loss: 1.0046

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5757 - loss: 1.0047

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5756 - loss: 1.0048

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5756 - loss: 1.0048

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5755 - loss: 1.0049

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 1.0049

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 1.0050

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 1.0050

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5753 - loss: 1.0051

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5752 - loss: 1.0052

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5752 - loss: 1.0052

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5752 - loss: 1.0052

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5751 - loss: 1.0053

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5750 - loss: 1.0053

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5750 - loss: 1.0054

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5749 - loss: 1.0054

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5749 - loss: 1.0055

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5748 - loss: 1.0055

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5748 - loss: 1.0056

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5747 - loss: 1.0056

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5747 - loss: 1.0057

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5747 - loss: 1.0057

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5746 - loss: 1.0058

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5746 - loss: 1.0058

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5745 - loss: 1.0058

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5744 - loss: 1.0059

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5744 - loss: 1.0059

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5743 - loss: 1.0060

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5743 - loss: 1.0061

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5742 - loss: 1.0061

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5742 - loss: 1.0062

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5741 - loss: 1.0062

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5741 - loss: 1.0063

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5740 - loss: 1.0063

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5740 - loss: 1.0064

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5739 - loss: 1.0064

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5739 - loss: 1.0065
Epoch 30: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5738 - loss: 1.0066 - val_accuracy: 0.5791 - val_loss: 0.9833 - learning_rate: 4.0000e-05
Epoch 31/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 163ms/step - accuracy: 0.4688 - loss: 1.0714

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4948 - loss: 1.0772   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5080 - loss: 1.0682

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5181 - loss: 1.0610

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5309 - loss: 1.0494

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5402 - loss: 1.0408

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5457 - loss: 1.0364

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5495 - loss: 1.0330

 24/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5516 - loss: 1.0316

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5538 - loss: 1.0293

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5569 - loss: 1.0265

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5601 - loss: 1.0234

 35/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5623 - loss: 1.0215

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5636 - loss: 1.0200

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5650 - loss: 1.0187

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5662 - loss: 1.0176

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5672 - loss: 1.0162

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0151

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0146

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0141

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5682 - loss: 1.0135

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5684 - loss: 1.0128

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5686 - loss: 1.0121

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5688 - loss: 1.0116

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5689 - loss: 1.0113

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5690 - loss: 1.0109

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5691 - loss: 1.0108

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5691 - loss: 1.0106

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5692 - loss: 1.0104

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5693 - loss: 1.0103

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5693 - loss: 1.0102

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5694 - loss: 1.0101

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0101

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0103

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0105

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0106

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0107

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0108

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0108

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5696 - loss: 1.0109

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5697 - loss: 1.0108

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5698 - loss: 1.0108

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5699 - loss: 1.0108

126/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5700 - loss: 1.0108

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5700 - loss: 1.0109

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0110

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0111

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5699 - loss: 1.0112

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0113

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0114

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0114

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0115

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0115

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5698 - loss: 1.0116

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5697 - loss: 1.0116

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5697 - loss: 1.0117

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5696 - loss: 1.0118

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5696 - loss: 1.0119

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5695 - loss: 1.0120

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5695 - loss: 1.0121

175/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0122

178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123

181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0124

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0125

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0126

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5692 - loss: 1.0126

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0126

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0126

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5693 - loss: 1.0125

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0124

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0124

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5694 - loss: 1.0123

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5695 - loss: 1.0122

225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0121

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0121

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5696 - loss: 1.0120

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5696 - loss: 1.0119

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0118

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0118

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0117

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0117

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0116

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5699 - loss: 1.0115

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5699 - loss: 1.0115

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0114

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0113

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0113

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0112

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0111

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0110

274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5702 - loss: 1.0110

277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5703 - loss: 1.0109

280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5703 - loss: 1.0108

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5704 - loss: 1.0108

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5704 - loss: 1.0107

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0107

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0106

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5705 - loss: 1.0106

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0105

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0105

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5706 - loss: 1.0104

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0104

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5707 - loss: 1.0103

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5708 - loss: 1.0103

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5708 - loss: 1.0103

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5708 - loss: 1.0102

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0102

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0102

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5709 - loss: 1.0101

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0101

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0101

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5710 - loss: 1.0100

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0100

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0099

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0098

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5711 - loss: 1.0098

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5711 - loss: 1.0098

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0098

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0097

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0096

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5712 - loss: 1.0095

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0095

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0095

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0094

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0093

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5713 - loss: 1.0093

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0093

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0093

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0092

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0091

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5714 - loss: 1.0090
Epoch 31: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5714 - loss: 1.0090 - val_accuracy: 0.5754 - val_loss: 0.9934 - learning_rate: 4.0000e-05
Epoch 32/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 161ms/step - accuracy: 0.4062 - loss: 1.1242

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4759 - loss: 1.0784   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4866 - loss: 1.0723

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4964 - loss: 1.0618

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5049 - loss: 1.0526

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5158 - loss: 1.0398

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5233 - loss: 1.0311

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5281 - loss: 1.0264

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5313 - loss: 1.0232

 28/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5337 - loss: 1.0211

 31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5354 - loss: 1.0202

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5363 - loss: 1.0201

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5374 - loss: 1.0197

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5378 - loss: 1.0201

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5380 - loss: 1.0207

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5382 - loss: 1.0211

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5386 - loss: 1.0217

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5388 - loss: 1.0224

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5395 - loss: 1.0225

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5400 - loss: 1.0227

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5404 - loss: 1.0230

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5409 - loss: 1.0231

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5415 - loss: 1.0231

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5421 - loss: 1.0230

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5428 - loss: 1.0229

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5434 - loss: 1.0227

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438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5598 - loss: 1.0144

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5599 - loss: 1.0144

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5599 - loss: 1.0143

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0143

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0143

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5600 - loss: 1.0142

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0142

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5601 - loss: 1.0141
Epoch 32: ReduceLROnPlateau reducing learning rate to 1e-05.
Epoch 32: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5602 - loss: 1.0140 - val_accuracy: 0.5767 - val_loss: 0.9849 - learning_rate: 4.0000e-05
Epoch 33/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5000 - loss: 1.0800

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5853 - loss: 1.0019   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5955 - loss: 0.9846

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6001 - loss: 0.9782

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6013 - loss: 0.9753

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5991 - loss: 0.9776

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5960 - loss: 0.9810

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5935 - loss: 0.9832

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5921 - loss: 0.9848

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5917 - loss: 0.9851

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5913 - loss: 0.9856

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5912 - loss: 0.9859

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5910 - loss: 0.9864

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5903 - loss: 0.9872

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9886

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5881 - loss: 0.9901

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5870 - loss: 0.9912

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5861 - loss: 0.9921

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5855 - loss: 0.9926

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5852 - loss: 0.9932

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5849 - loss: 0.9935

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5846 - loss: 0.9938

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5841 - loss: 0.9946

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5835 - loss: 0.9954

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5829 - loss: 0.9961

 75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5824 - loss: 0.9966

 78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5819 - loss: 0.9970

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5813 - loss: 0.9973

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9976

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5805 - loss: 0.9978

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5801 - loss: 0.9980

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5797 - loss: 0.9983

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5793 - loss: 0.9985

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5788 - loss: 0.9987

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5785 - loss: 0.9988

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5782 - loss: 0.9991

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5778 - loss: 0.9992

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5776 - loss: 0.9993

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5773 - loss: 0.9993

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9994

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 0.9994

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9994

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9993

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9993

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9993

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9992

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 0.9992

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5764 - loss: 0.9992

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5764 - loss: 0.9991

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9990

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9989

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9988

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9987

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9986

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9985

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9984

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9984

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9983

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9982

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9982

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5763 - loss: 0.9982

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 0.9981

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 0.9980

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9979

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9979

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9978

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9977

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9976

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9976

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9975

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5762 - loss: 0.9974

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5763 - loss: 0.9974

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5763 - loss: 0.9974

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5763 - loss: 0.9973

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5763 - loss: 0.9973

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5763 - loss: 0.9973

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9972

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5764 - loss: 0.9971

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9971

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5764 - loss: 0.9971

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9970

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9968

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5765 - loss: 0.9969

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9969

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5765 - loss: 0.9970

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9970

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9970

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5764 - loss: 0.9971

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309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9972

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9973

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5763 - loss: 0.9973

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9974

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9974

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9975

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5762 - loss: 0.9975

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9976

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9977

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9977

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5761 - loss: 0.9978

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9978

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9979

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9980

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5760 - loss: 0.9980

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9981

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9981

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9982

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5759 - loss: 0.9982

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5758 - loss: 0.9983

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5758 - loss: 0.9983

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5758 - loss: 0.9984

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5758 - loss: 0.9984

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9985

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5757 - loss: 0.9986

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9986

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9987

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9987

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5756 - loss: 0.9988

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9988

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9988

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9989

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5755 - loss: 0.9989

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 0.9989

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5754 - loss: 0.9990

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5754 - loss: 0.9990

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5754 - loss: 0.9991

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5753 - loss: 0.9991

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5753 - loss: 0.9992

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5753 - loss: 0.9992

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9993

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9993

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9994

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5752 - loss: 0.9994

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9994

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5751 - loss: 0.9995

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9996

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9996

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5750 - loss: 0.9997
Epoch 33: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5749 - loss: 0.9998 - val_accuracy: 0.5799 - val_loss: 0.9842 - learning_rate: 1.0000e-05
Epoch 34/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.5312 - loss: 1.1022

  3/473 ━━━━━━━━━━━━━━━━━━━━ 16s 35ms/step - accuracy: 0.5469 - loss: 1.0521  

  6/473 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5495 - loss: 1.0595

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5619 - loss: 1.0464

 12/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5664 - loss: 1.0368

 14/473 ━━━━━━━━━━━━━━━━━━━━ 11s 25ms/step - accuracy: 0.5672 - loss: 1.0333

 17/473 ━━━━━━━━━━━━━━━━━━━━ 10s 24ms/step - accuracy: 0.5681 - loss: 1.0304

 20/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5689 - loss: 1.0267

 23/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5703 - loss: 1.0237

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5717 - loss: 1.0212 

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5730 - loss: 1.0192

 32/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5739 - loss: 1.0175

 34/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5744 - loss: 1.0167

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.5747 - loss: 1.0163 

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5748 - loss: 1.0160

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5749 - loss: 1.0155

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5751 - loss: 1.0140

 50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5755 - loss: 1.0124

 52/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5758 - loss: 1.0113

 55/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5764 - loss: 1.0098

 58/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5768 - loss: 1.0082

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 1.0070

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5771 - loss: 1.0062

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0057

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5770 - loss: 1.0053

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5771 - loss: 1.0047

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0042

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0038

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5770 - loss: 1.0033

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5771 - loss: 1.0028

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0023

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0018

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 1.0015

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 1.0013

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 1.0012

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5768 - loss: 1.0010

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5767 - loss: 1.0009

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 1.0007

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 1.0005

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 1.0003

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5762 - loss: 1.0000

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9997

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5760 - loss: 0.9995

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5759 - loss: 0.9993

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5757 - loss: 0.9991

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5756 - loss: 0.9990

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5755 - loss: 0.9988

135/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5754 - loss: 0.9987

138/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5753 - loss: 0.9986

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5753 - loss: 0.9985

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9983

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9982

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9980

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9979

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9978

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9977

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9976

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9975

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 0.9975

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9975

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9974

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5751 - loss: 0.9974

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9974

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9973

184/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 0.9973

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9973

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9972

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5750 - loss: 0.9971

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Epoch 34: val_accuracy did not improve from 0.58348

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5758 - loss: 0.9966 - val_accuracy: 0.5799 - val_loss: 0.9852 - learning_rate: 1.0000e-05
Epoch 34: early stopping
Restoring model weights from the end of the best epoch: 27.

Plotting the Training and Validation Accuracies¶

In [48]:
plt.plot(history_resnet.history["accuracy"])
plt.plot(history_resnet.history["val_accuracy"])
plt.title("ResNet50V2 Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the ResNet Model¶

In [49]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_resnet.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator_resnet.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = new_resnet_model.evaluate(test_generator_resnet, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6250 - loss: 0.9257

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5740 - loss: 0.9644 
Loss: 0.9330559968948364, Accuracy: 0.578125

Plotting Confusion Matrix¶

In [50]:
pred_probabilities = new_resnet_model.predict(test_generator_resnet, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator_resnet.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("ResNet50V2 Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 2s 866ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step  
              precision    recall  f1-score   support

       happy       0.57      0.62      0.60        32
     neutral       0.40      0.53      0.45        32
         sad       0.71      0.38      0.49        32
    surprise       0.76      0.78      0.77        32

    accuracy                           0.58       128
   macro avg       0.61      0.58      0.58       128
weighted avg       0.61      0.58      0.58       128

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Observations and Insights:

  • The ResNet50V2 model was customized for the task, using 1,453,568 total parameters. We cut the original model at the 'conv3_block4_out' layer, as we are dealing with low-resolution images
  • The test accuracy achieved was 57.81%, indicating a modest performance in predicting the facial emotions on unseen images.
  • The model had varying success with different emotions, performing best on 'surprise' with an f1-score of 0.77, and least effectively on 'neutral', with a lower f1-score of 0.45.
  • We have chosen also the ResNet50V2 over the other models from this family (ResNet101V2 and ResNet152V2) for the size being smaller and the computational efficiency of the model, which we found more suitable for the task we had to accomplish.

EfficientNet Model¶

In [51]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [52]:
efficient_model = EfficientNetV2B0(
    weights="imagenet", include_top=False, input_shape=(img_width, img_height, color_layers)
)
# Making all the layers of the efficient_model model non-trainable. i.e. freezing them
for layer in efficient_model.layers:
    layer.trainable = False

efficient_model.summary()
Model: "efficientnetv2-b0"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ input_layer         │ (None, 48, 48, 3) │          0 │ -                 │
│ (InputLayer)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ rescaling           │ (None, 48, 48, 3) │          0 │ input_layer[0][0] │
│ (Rescaling)         │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ normalization       │ (None, 48, 48, 3) │          0 │ rescaling[0][0]   │
│ (Normalization)     │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ stem_conv (Conv2D)  │ (None, 24, 24,    │        864 │ normalization[0]… │
│                     │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ stem_bn             │ (None, 24, 24,    │        128 │ stem_conv[0][0]   │
│ (BatchNormalizatio… │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ stem_activation     │ (None, 24, 24,    │          0 │ stem_bn[0][0]     │
│ (Activation)        │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block1a_project_co… │ (None, 24, 24,    │      4,608 │ stem_activation[… │
│ (Conv2D)            │ 16)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block1a_project_bn  │ (None, 24, 24,    │         64 │ block1a_project_… │
│ (BatchNormalizatio… │ 16)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block1a_project_ac… │ (None, 24, 24,    │          0 │ block1a_project_… │
│ (Activation)        │ 16)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2a_expand_conv │ (None, 12, 12,    │      9,216 │ block1a_project_… │
│ (Conv2D)            │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2a_expand_bn   │ (None, 12, 12,    │        256 │ block2a_expand_c… │
│ (BatchNormalizatio… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2a_expand_act… │ (None, 12, 12,    │          0 │ block2a_expand_b… │
│ (Activation)        │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2a_project_co… │ (None, 12, 12,    │      2,048 │ block2a_expand_a… │
│ (Conv2D)            │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2a_project_bn  │ (None, 12, 12,    │        128 │ block2a_project_… │
│ (BatchNormalizatio… │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_expand_conv │ (None, 12, 12,    │     36,864 │ block2a_project_… │
│ (Conv2D)            │ 128)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_expand_bn   │ (None, 12, 12,    │        512 │ block2b_expand_c… │
│ (BatchNormalizatio… │ 128)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_expand_act… │ (None, 12, 12,    │          0 │ block2b_expand_b… │
│ (Activation)        │ 128)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_project_co… │ (None, 12, 12,    │      4,096 │ block2b_expand_a… │
│ (Conv2D)            │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_project_bn  │ (None, 12, 12,    │        128 │ block2b_project_… │
│ (BatchNormalizatio… │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_drop        │ (None, 12, 12,    │          0 │ block2b_project_… │
│ (Dropout)           │ 32)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block2b_add (Add)   │ (None, 12, 12,    │          0 │ block2b_drop[0][… │
│                     │ 32)               │            │ block2a_project_… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3a_expand_conv │ (None, 6, 6, 128) │     36,864 │ block2b_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3a_expand_bn   │ (None, 6, 6, 128) │        512 │ block3a_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3a_expand_act… │ (None, 6, 6, 128) │          0 │ block3a_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3a_project_co… │ (None, 6, 6, 48)  │      6,144 │ block3a_expand_a… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3a_project_bn  │ (None, 6, 6, 48)  │        192 │ block3a_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_expand_conv │ (None, 6, 6, 192) │     82,944 │ block3a_project_… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_expand_bn   │ (None, 6, 6, 192) │        768 │ block3b_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_expand_act… │ (None, 6, 6, 192) │          0 │ block3b_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_project_co… │ (None, 6, 6, 48)  │      9,216 │ block3b_expand_a… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_project_bn  │ (None, 6, 6, 48)  │        192 │ block3b_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_drop        │ (None, 6, 6, 48)  │          0 │ block3b_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block3b_add (Add)   │ (None, 6, 6, 48)  │          0 │ block3b_drop[0][… │
│                     │                   │            │ block3a_project_… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_expand_conv │ (None, 6, 6, 192) │      9,216 │ block3b_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_expand_bn   │ (None, 6, 6, 192) │        768 │ block4a_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_expand_act… │ (None, 6, 6, 192) │          0 │ block4a_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_dwconv2     │ (None, 3, 3, 192) │      1,728 │ block4a_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_bn          │ (None, 3, 3, 192) │        768 │ block4a_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_activation  │ (None, 3, 3, 192) │          0 │ block4a_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_se_squeeze  │ (None, 192)       │          0 │ block4a_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_se_reshape  │ (None, 1, 1, 192) │          0 │ block4a_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_se_reduce   │ (None, 1, 1, 12)  │      2,316 │ block4a_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_se_expand   │ (None, 1, 1, 192) │      2,496 │ block4a_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_se_excite   │ (None, 3, 3, 192) │          0 │ block4a_activati… │
│ (Multiply)          │                   │            │ block4a_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_project_co… │ (None, 3, 3, 96)  │     18,432 │ block4a_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4a_project_bn  │ (None, 3, 3, 96)  │        384 │ block4a_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_expand_conv │ (None, 3, 3, 384) │     36,864 │ block4a_project_… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_expand_bn   │ (None, 3, 3, 384) │      1,536 │ block4b_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_expand_act… │ (None, 3, 3, 384) │          0 │ block4b_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_dwconv2     │ (None, 3, 3, 384) │      3,456 │ block4b_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_bn          │ (None, 3, 3, 384) │      1,536 │ block4b_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_activation  │ (None, 3, 3, 384) │          0 │ block4b_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_se_squeeze  │ (None, 384)       │          0 │ block4b_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_se_reshape  │ (None, 1, 1, 384) │          0 │ block4b_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_se_reduce   │ (None, 1, 1, 24)  │      9,240 │ block4b_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_se_expand   │ (None, 1, 1, 384) │      9,600 │ block4b_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_se_excite   │ (None, 3, 3, 384) │          0 │ block4b_activati… │
│ (Multiply)          │                   │            │ block4b_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_project_co… │ (None, 3, 3, 96)  │     36,864 │ block4b_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_project_bn  │ (None, 3, 3, 96)  │        384 │ block4b_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_drop        │ (None, 3, 3, 96)  │          0 │ block4b_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4b_add (Add)   │ (None, 3, 3, 96)  │          0 │ block4b_drop[0][… │
│                     │                   │            │ block4a_project_… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_expand_conv │ (None, 3, 3, 384) │     36,864 │ block4b_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_expand_bn   │ (None, 3, 3, 384) │      1,536 │ block4c_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_expand_act… │ (None, 3, 3, 384) │          0 │ block4c_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_dwconv2     │ (None, 3, 3, 384) │      3,456 │ block4c_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_bn          │ (None, 3, 3, 384) │      1,536 │ block4c_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_activation  │ (None, 3, 3, 384) │          0 │ block4c_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_se_squeeze  │ (None, 384)       │          0 │ block4c_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_se_reshape  │ (None, 1, 1, 384) │          0 │ block4c_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_se_reduce   │ (None, 1, 1, 24)  │      9,240 │ block4c_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_se_expand   │ (None, 1, 1, 384) │      9,600 │ block4c_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_se_excite   │ (None, 3, 3, 384) │          0 │ block4c_activati… │
│ (Multiply)          │                   │            │ block4c_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_project_co… │ (None, 3, 3, 96)  │     36,864 │ block4c_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_project_bn  │ (None, 3, 3, 96)  │        384 │ block4c_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_drop        │ (None, 3, 3, 96)  │          0 │ block4c_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block4c_add (Add)   │ (None, 3, 3, 96)  │          0 │ block4c_drop[0][… │
│                     │                   │            │ block4b_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_expand_conv │ (None, 3, 3, 576) │     55,296 │ block4c_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_expand_bn   │ (None, 3, 3, 576) │      2,304 │ block5a_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_expand_act… │ (None, 3, 3, 576) │          0 │ block5a_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_dwconv2     │ (None, 3, 3, 576) │      5,184 │ block5a_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_bn          │ (None, 3, 3, 576) │      2,304 │ block5a_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_activation  │ (None, 3, 3, 576) │          0 │ block5a_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_se_squeeze  │ (None, 576)       │          0 │ block5a_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_se_reshape  │ (None, 1, 1, 576) │          0 │ block5a_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_se_reduce   │ (None, 1, 1, 24)  │     13,848 │ block5a_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_se_expand   │ (None, 1, 1, 576) │     14,400 │ block5a_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_se_excite   │ (None, 3, 3, 576) │          0 │ block5a_activati… │
│ (Multiply)          │                   │            │ block5a_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_project_co… │ (None, 3, 3, 112) │     64,512 │ block5a_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5a_project_bn  │ (None, 3, 3, 112) │        448 │ block5a_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_expand_conv │ (None, 3, 3, 672) │     75,264 │ block5a_project_… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_expand_bn   │ (None, 3, 3, 672) │      2,688 │ block5b_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_expand_act… │ (None, 3, 3, 672) │          0 │ block5b_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_dwconv2     │ (None, 3, 3, 672) │      6,048 │ block5b_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_bn          │ (None, 3, 3, 672) │      2,688 │ block5b_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_activation  │ (None, 3, 3, 672) │          0 │ block5b_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_se_squeeze  │ (None, 672)       │          0 │ block5b_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_se_reshape  │ (None, 1, 1, 672) │          0 │ block5b_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_se_reduce   │ (None, 1, 1, 28)  │     18,844 │ block5b_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_se_expand   │ (None, 1, 1, 672) │     19,488 │ block5b_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_se_excite   │ (None, 3, 3, 672) │          0 │ block5b_activati… │
│ (Multiply)          │                   │            │ block5b_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_project_co… │ (None, 3, 3, 112) │     75,264 │ block5b_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_project_bn  │ (None, 3, 3, 112) │        448 │ block5b_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_drop        │ (None, 3, 3, 112) │          0 │ block5b_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5b_add (Add)   │ (None, 3, 3, 112) │          0 │ block5b_drop[0][… │
│                     │                   │            │ block5a_project_… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_expand_conv │ (None, 3, 3, 672) │     75,264 │ block5b_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_expand_bn   │ (None, 3, 3, 672) │      2,688 │ block5c_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_expand_act… │ (None, 3, 3, 672) │          0 │ block5c_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_dwconv2     │ (None, 3, 3, 672) │      6,048 │ block5c_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_bn          │ (None, 3, 3, 672) │      2,688 │ block5c_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_activation  │ (None, 3, 3, 672) │          0 │ block5c_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_se_squeeze  │ (None, 672)       │          0 │ block5c_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_se_reshape  │ (None, 1, 1, 672) │          0 │ block5c_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_se_reduce   │ (None, 1, 1, 28)  │     18,844 │ block5c_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_se_expand   │ (None, 1, 1, 672) │     19,488 │ block5c_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_se_excite   │ (None, 3, 3, 672) │          0 │ block5c_activati… │
│ (Multiply)          │                   │            │ block5c_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_project_co… │ (None, 3, 3, 112) │     75,264 │ block5c_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_project_bn  │ (None, 3, 3, 112) │        448 │ block5c_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_drop        │ (None, 3, 3, 112) │          0 │ block5c_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5c_add (Add)   │ (None, 3, 3, 112) │          0 │ block5c_drop[0][… │
│                     │                   │            │ block5b_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_expand_conv │ (None, 3, 3, 672) │     75,264 │ block5c_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_expand_bn   │ (None, 3, 3, 672) │      2,688 │ block5d_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_expand_act… │ (None, 3, 3, 672) │          0 │ block5d_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_dwconv2     │ (None, 3, 3, 672) │      6,048 │ block5d_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_bn          │ (None, 3, 3, 672) │      2,688 │ block5d_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_activation  │ (None, 3, 3, 672) │          0 │ block5d_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_se_squeeze  │ (None, 672)       │          0 │ block5d_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_se_reshape  │ (None, 1, 1, 672) │          0 │ block5d_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_se_reduce   │ (None, 1, 1, 28)  │     18,844 │ block5d_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_se_expand   │ (None, 1, 1, 672) │     19,488 │ block5d_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_se_excite   │ (None, 3, 3, 672) │          0 │ block5d_activati… │
│ (Multiply)          │                   │            │ block5d_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_project_co… │ (None, 3, 3, 112) │     75,264 │ block5d_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_project_bn  │ (None, 3, 3, 112) │        448 │ block5d_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_drop        │ (None, 3, 3, 112) │          0 │ block5d_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5d_add (Add)   │ (None, 3, 3, 112) │          0 │ block5d_drop[0][… │
│                     │                   │            │ block5c_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_expand_conv │ (None, 3, 3, 672) │     75,264 │ block5d_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_expand_bn   │ (None, 3, 3, 672) │      2,688 │ block5e_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_expand_act… │ (None, 3, 3, 672) │          0 │ block5e_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_dwconv2     │ (None, 3, 3, 672) │      6,048 │ block5e_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_bn          │ (None, 3, 3, 672) │      2,688 │ block5e_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_activation  │ (None, 3, 3, 672) │          0 │ block5e_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_se_squeeze  │ (None, 672)       │          0 │ block5e_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_se_reshape  │ (None, 1, 1, 672) │          0 │ block5e_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_se_reduce   │ (None, 1, 1, 28)  │     18,844 │ block5e_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_se_expand   │ (None, 1, 1, 672) │     19,488 │ block5e_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_se_excite   │ (None, 3, 3, 672) │          0 │ block5e_activati… │
│ (Multiply)          │                   │            │ block5e_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_project_co… │ (None, 3, 3, 112) │     75,264 │ block5e_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_project_bn  │ (None, 3, 3, 112) │        448 │ block5e_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_drop        │ (None, 3, 3, 112) │          0 │ block5e_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block5e_add (Add)   │ (None, 3, 3, 112) │          0 │ block5e_drop[0][… │
│                     │                   │            │ block5d_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_expand_conv │ (None, 3, 3, 672) │     75,264 │ block5e_add[0][0] │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_expand_bn   │ (None, 3, 3, 672) │      2,688 │ block6a_expand_c… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_expand_act… │ (None, 3, 3, 672) │          0 │ block6a_expand_b… │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_dwconv2     │ (None, 2, 2, 672) │      6,048 │ block6a_expand_a… │
│ (DepthwiseConv2D)   │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_bn          │ (None, 2, 2, 672) │      2,688 │ block6a_dwconv2[… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_activation  │ (None, 2, 2, 672) │          0 │ block6a_bn[0][0]  │
│ (Activation)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_se_squeeze  │ (None, 672)       │          0 │ block6a_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_se_reshape  │ (None, 1, 1, 672) │          0 │ block6a_se_squee… │
│ (Reshape)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_se_reduce   │ (None, 1, 1, 28)  │     18,844 │ block6a_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_se_expand   │ (None, 1, 1, 672) │     19,488 │ block6a_se_reduc… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_se_excite   │ (None, 2, 2, 672) │          0 │ block6a_activati… │
│ (Multiply)          │                   │            │ block6a_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_project_co… │ (None, 2, 2, 192) │    129,024 │ block6a_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6a_project_bn  │ (None, 2, 2, 192) │        768 │ block6a_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_expand_conv │ (None, 2, 2,      │    221,184 │ block6a_project_… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_expand_bn   │ (None, 2, 2,      │      4,608 │ block6b_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_expand_act… │ (None, 2, 2,      │          0 │ block6b_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_dwconv2     │ (None, 2, 2,      │     10,368 │ block6b_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_bn          │ (None, 2, 2,      │      4,608 │ block6b_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_activation  │ (None, 2, 2,      │          0 │ block6b_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_se_squeeze  │ (None, 1152)      │          0 │ block6b_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_se_reshape  │ (None, 1, 1,      │          0 │ block6b_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6b_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_se_expand   │ (None, 1, 1,      │     56,448 │ block6b_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_se_excite   │ (None, 2, 2,      │          0 │ block6b_activati… │
│ (Multiply)          │ 1152)             │            │ block6b_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_project_co… │ (None, 2, 2, 192) │    221,184 │ block6b_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_project_bn  │ (None, 2, 2, 192) │        768 │ block6b_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_drop        │ (None, 2, 2, 192) │          0 │ block6b_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6b_add (Add)   │ (None, 2, 2, 192) │          0 │ block6b_drop[0][… │
│                     │                   │            │ block6a_project_… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_expand_conv │ (None, 2, 2,      │    221,184 │ block6b_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_expand_bn   │ (None, 2, 2,      │      4,608 │ block6c_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_expand_act… │ (None, 2, 2,      │          0 │ block6c_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_dwconv2     │ (None, 2, 2,      │     10,368 │ block6c_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_bn          │ (None, 2, 2,      │      4,608 │ block6c_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_activation  │ (None, 2, 2,      │          0 │ block6c_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_se_squeeze  │ (None, 1152)      │          0 │ block6c_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_se_reshape  │ (None, 1, 1,      │          0 │ block6c_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6c_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_se_expand   │ (None, 1, 1,      │     56,448 │ block6c_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_se_excite   │ (None, 2, 2,      │          0 │ block6c_activati… │
│ (Multiply)          │ 1152)             │            │ block6c_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_project_co… │ (None, 2, 2, 192) │    221,184 │ block6c_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_project_bn  │ (None, 2, 2, 192) │        768 │ block6c_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_drop        │ (None, 2, 2, 192) │          0 │ block6c_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6c_add (Add)   │ (None, 2, 2, 192) │          0 │ block6c_drop[0][… │
│                     │                   │            │ block6b_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_expand_conv │ (None, 2, 2,      │    221,184 │ block6c_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_expand_bn   │ (None, 2, 2,      │      4,608 │ block6d_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_expand_act… │ (None, 2, 2,      │          0 │ block6d_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_dwconv2     │ (None, 2, 2,      │     10,368 │ block6d_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_bn          │ (None, 2, 2,      │      4,608 │ block6d_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_activation  │ (None, 2, 2,      │          0 │ block6d_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_se_squeeze  │ (None, 1152)      │          0 │ block6d_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_se_reshape  │ (None, 1, 1,      │          0 │ block6d_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6d_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_se_expand   │ (None, 1, 1,      │     56,448 │ block6d_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_se_excite   │ (None, 2, 2,      │          0 │ block6d_activati… │
│ (Multiply)          │ 1152)             │            │ block6d_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_project_co… │ (None, 2, 2, 192) │    221,184 │ block6d_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_project_bn  │ (None, 2, 2, 192) │        768 │ block6d_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_drop        │ (None, 2, 2, 192) │          0 │ block6d_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6d_add (Add)   │ (None, 2, 2, 192) │          0 │ block6d_drop[0][… │
│                     │                   │            │ block6c_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_expand_conv │ (None, 2, 2,      │    221,184 │ block6d_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_expand_bn   │ (None, 2, 2,      │      4,608 │ block6e_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_expand_act… │ (None, 2, 2,      │          0 │ block6e_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_dwconv2     │ (None, 2, 2,      │     10,368 │ block6e_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_bn          │ (None, 2, 2,      │      4,608 │ block6e_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_activation  │ (None, 2, 2,      │          0 │ block6e_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_se_squeeze  │ (None, 1152)      │          0 │ block6e_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_se_reshape  │ (None, 1, 1,      │          0 │ block6e_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6e_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_se_expand   │ (None, 1, 1,      │     56,448 │ block6e_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_se_excite   │ (None, 2, 2,      │          0 │ block6e_activati… │
│ (Multiply)          │ 1152)             │            │ block6e_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_project_co… │ (None, 2, 2, 192) │    221,184 │ block6e_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_project_bn  │ (None, 2, 2, 192) │        768 │ block6e_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_drop        │ (None, 2, 2, 192) │          0 │ block6e_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6e_add (Add)   │ (None, 2, 2, 192) │          0 │ block6e_drop[0][… │
│                     │                   │            │ block6d_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_expand_conv │ (None, 2, 2,      │    221,184 │ block6e_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_expand_bn   │ (None, 2, 2,      │      4,608 │ block6f_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_expand_act… │ (None, 2, 2,      │          0 │ block6f_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_dwconv2     │ (None, 2, 2,      │     10,368 │ block6f_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_bn          │ (None, 2, 2,      │      4,608 │ block6f_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_activation  │ (None, 2, 2,      │          0 │ block6f_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_se_squeeze  │ (None, 1152)      │          0 │ block6f_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_se_reshape  │ (None, 1, 1,      │          0 │ block6f_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6f_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_se_expand   │ (None, 1, 1,      │     56,448 │ block6f_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_se_excite   │ (None, 2, 2,      │          0 │ block6f_activati… │
│ (Multiply)          │ 1152)             │            │ block6f_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_project_co… │ (None, 2, 2, 192) │    221,184 │ block6f_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_project_bn  │ (None, 2, 2, 192) │        768 │ block6f_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_drop        │ (None, 2, 2, 192) │          0 │ block6f_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6f_add (Add)   │ (None, 2, 2, 192) │          0 │ block6f_drop[0][… │
│                     │                   │            │ block6e_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_expand_conv │ (None, 2, 2,      │    221,184 │ block6f_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_expand_bn   │ (None, 2, 2,      │      4,608 │ block6g_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_expand_act… │ (None, 2, 2,      │          0 │ block6g_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_dwconv2     │ (None, 2, 2,      │     10,368 │ block6g_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_bn          │ (None, 2, 2,      │      4,608 │ block6g_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_activation  │ (None, 2, 2,      │          0 │ block6g_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_se_squeeze  │ (None, 1152)      │          0 │ block6g_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_se_reshape  │ (None, 1, 1,      │          0 │ block6g_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6g_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_se_expand   │ (None, 1, 1,      │     56,448 │ block6g_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_se_excite   │ (None, 2, 2,      │          0 │ block6g_activati… │
│ (Multiply)          │ 1152)             │            │ block6g_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_project_co… │ (None, 2, 2, 192) │    221,184 │ block6g_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_project_bn  │ (None, 2, 2, 192) │        768 │ block6g_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_drop        │ (None, 2, 2, 192) │          0 │ block6g_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6g_add (Add)   │ (None, 2, 2, 192) │          0 │ block6g_drop[0][… │
│                     │                   │            │ block6f_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_expand_conv │ (None, 2, 2,      │    221,184 │ block6g_add[0][0] │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_expand_bn   │ (None, 2, 2,      │      4,608 │ block6h_expand_c… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_expand_act… │ (None, 2, 2,      │          0 │ block6h_expand_b… │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_dwconv2     │ (None, 2, 2,      │     10,368 │ block6h_expand_a… │
│ (DepthwiseConv2D)   │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_bn          │ (None, 2, 2,      │      4,608 │ block6h_dwconv2[… │
│ (BatchNormalizatio… │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_activation  │ (None, 2, 2,      │          0 │ block6h_bn[0][0]  │
│ (Activation)        │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_se_squeeze  │ (None, 1152)      │          0 │ block6h_activati… │
│ (GlobalAveragePool… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_se_reshape  │ (None, 1, 1,      │          0 │ block6h_se_squee… │
│ (Reshape)           │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_se_reduce   │ (None, 1, 1, 48)  │     55,344 │ block6h_se_resha… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_se_expand   │ (None, 1, 1,      │     56,448 │ block6h_se_reduc… │
│ (Conv2D)            │ 1152)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_se_excite   │ (None, 2, 2,      │          0 │ block6h_activati… │
│ (Multiply)          │ 1152)             │            │ block6h_se_expan… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_project_co… │ (None, 2, 2, 192) │    221,184 │ block6h_se_excit… │
│ (Conv2D)            │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_project_bn  │ (None, 2, 2, 192) │        768 │ block6h_project_… │
│ (BatchNormalizatio… │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_drop        │ (None, 2, 2, 192) │          0 │ block6h_project_… │
│ (Dropout)           │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ block6h_add (Add)   │ (None, 2, 2, 192) │          0 │ block6h_drop[0][… │
│                     │                   │            │ block6g_add[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ top_conv (Conv2D)   │ (None, 2, 2,      │    245,760 │ block6h_add[0][0] │
│                     │ 1280)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ top_bn              │ (None, 2, 2,      │      5,120 │ top_conv[0][0]    │
│ (BatchNormalizatio… │ 1280)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ top_activation      │ (None, 2, 2,      │          0 │ top_bn[0][0]      │
│ (Activation)        │ 1280)             │            │                   │
└─────────────────────┴───────────────────┴────────────┴───────────────────┘
 Total params: 5,919,312 (22.58 MB)
 Trainable params: 0 (0.00 B)
 Non-trainable params: 5,919,312 (22.58 MB)

Model Building¶

In [53]:
new_efficient_model = Sequential()
new_efficient_model.add(efficient_model)

# Reduces each feature map to a single value by averaging all elements
new_efficient_model.add(GlobalAveragePooling2D())

# Adding full connected layers
new_efficient_model.add(Dense(256, activation="relu"))
new_efficient_model.add(Dense(128, activation="relu"))

# Output Layer
new_efficient_model.add(Dense(4, activation="softmax"))

# Using Adam Optimizer
optimizer = Adam(learning_rate=0.01)

Compiling and Training the Model¶

In [54]:
new_efficient_model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
new_efficient_model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ efficientnetv2-b0 (Functional)  │ ?                      │     5,919,312 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d        │ ?                      │   0 (unbuilt) │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 5,919,312 (22.58 MB)
 Trainable params: 0 (0.00 B)
 Non-trainable params: 5,919,312 (22.58 MB)
In [55]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 20 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=20
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

# Define the saving the best model callback
mc = ModelCheckpoint(
    f"{results_path}/best_model_efficient_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 40 epochs and using validation set
history_efficient = new_efficient_model.fit(
    train_generator_efficientnet,
    epochs=40,
    validation_data=validation_generator_efficientnet,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/40
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
I0000 00:00:1712795242.855338 1509123 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 32 bytes spill stores, 32 bytes spill loads

I0000 00:00:1712795242.912001 1509129 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 40 bytes spill stores, 40 bytes spill loads

I0000 00:00:1712795243.210471 1509126 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 32 bytes spill stores, 32 bytes spill loads

I0000 00:00:1712795243.323623 1509129 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11837', 4 bytes spill stores, 4 bytes spill loads

I0000 00:00:1712795243.486678 1509130 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11858', 4 bytes spill stores, 4 bytes spill loads

I0000 00:00:1712795243.743913 1509132 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 244 bytes spill stores, 244 bytes spill loads

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I0000 00:00:1712795261.341273 1509630 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_11548', 20 bytes spill stores, 20 bytes spill loads


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I0000 00:00:1712795281.118442 1510282 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_2315', 260 bytes spill stores, 260 bytes spill loads

Epoch 1: val_accuracy improved from -inf to 0.55515, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 55s 69ms/step - accuracy: 0.4291 - loss: 1.3089 - val_accuracy: 0.5552 - val_loss: 1.0737 - learning_rate: 0.0100
Epoch 2/40
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Epoch 2: val_accuracy improved from 0.55515 to 0.56339, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 3/40
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Epoch 3: val_accuracy improved from 0.56339 to 0.56701, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5086 - loss: 1.1278 - val_accuracy: 0.5670 - val_loss: 1.0315 - learning_rate: 0.0100
Epoch 4/40
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365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5198 - loss: 1.1023

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371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5196 - loss: 1.1026

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5196 - loss: 1.1027

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5195 - loss: 1.1028

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5194 - loss: 1.1029

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5194 - loss: 1.1031

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5193 - loss: 1.1032

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5192 - loss: 1.1033

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5192 - loss: 1.1034

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5191 - loss: 1.1035

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5191 - loss: 1.1036

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5190 - loss: 1.1038

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5190 - loss: 1.1039

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5189 - loss: 1.1040

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5189 - loss: 1.1041

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5188 - loss: 1.1042

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5188 - loss: 1.1043

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5187 - loss: 1.1044

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5187 - loss: 1.1045

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5186 - loss: 1.1046

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5186 - loss: 1.1047

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5185 - loss: 1.1048

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5185 - loss: 1.1049

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1050

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1052

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5184 - loss: 1.1052

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5183 - loss: 1.1053

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5183 - loss: 1.1054

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5182 - loss: 1.1055

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5182 - loss: 1.1056

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1057

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1059

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5181 - loss: 1.1059

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5180 - loss: 1.1060

473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5179 - loss: 1.1063
Epoch 4: val_accuracy did not improve from 0.56701

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5179 - loss: 1.1063 - val_accuracy: 0.5584 - val_loss: 1.0353 - learning_rate: 0.0100
Epoch 5/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 165ms/step - accuracy: 0.6250 - loss: 0.9951

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5807 - loss: 0.9968   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5690 - loss: 1.0114

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5570 - loss: 1.0343

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5476 - loss: 1.0543

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5409 - loss: 1.0699

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5370 - loss: 1.0826

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5338 - loss: 1.0913

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5302 - loss: 1.0983

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5276 - loss: 1.1039

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5255 - loss: 1.1087

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5237 - loss: 1.1124

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5224 - loss: 1.1156

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5216 - loss: 1.1178

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5209 - loss: 1.1195

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5206 - loss: 1.1206

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5205 - loss: 1.1211

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5207 - loss: 1.1211

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5210 - loss: 1.1209

 58/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5213 - loss: 1.1207

 61/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5214 - loss: 1.1204

 64/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5216 - loss: 1.1202

 67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1201

 70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1202

 73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5217 - loss: 1.1204

 75/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5216 - loss: 1.1206

 77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5214 - loss: 1.1207

 80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5211 - loss: 1.1211

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5208 - loss: 1.1216

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5206 - loss: 1.1220

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5203 - loss: 1.1224

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5201 - loss: 1.1227

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5198 - loss: 1.1231

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5196 - loss: 1.1235

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5194 - loss: 1.1240

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5191 - loss: 1.1244

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5189 - loss: 1.1248

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5187 - loss: 1.1253

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5185 - loss: 1.1256

116/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5182 - loss: 1.1259

119/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5179 - loss: 1.1263

122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5176 - loss: 1.1266

125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5174 - loss: 1.1269

128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5171 - loss: 1.1272

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5168 - loss: 1.1274

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5166 - loss: 1.1276

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5164 - loss: 1.1278

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5161 - loss: 1.1280

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5160 - loss: 1.1282

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5158 - loss: 1.1283

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5157 - loss: 1.1284

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5155 - loss: 1.1285

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5153 - loss: 1.1286

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5152 - loss: 1.1286

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5151 - loss: 1.1287

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5150 - loss: 1.1287

167/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5149 - loss: 1.1288

170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5147 - loss: 1.1289

173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5146 - loss: 1.1290

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5144 - loss: 1.1291

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5143 - loss: 1.1292

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5141 - loss: 1.1293

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5140 - loss: 1.1293

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5139 - loss: 1.1293

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5138 - loss: 1.1294

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5137 - loss: 1.1294

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5136 - loss: 1.1295

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5135 - loss: 1.1296

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5134 - loss: 1.1296

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5133 - loss: 1.1296

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5132 - loss: 1.1296

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5131 - loss: 1.1297

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5131 - loss: 1.1297

218/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5130 - loss: 1.1297

221/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5130 - loss: 1.1297

224/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5129 - loss: 1.1296

227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5129 - loss: 1.1296

230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5128 - loss: 1.1296

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5128 - loss: 1.1296

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 19ms/step - accuracy: 0.5127 - loss: 1.1296

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5127 - loss: 1.1296

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5126 - loss: 1.1296

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5126 - loss: 1.1296

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5125 - loss: 1.1295

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1294

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5124 - loss: 1.1293

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5123 - loss: 1.1293

273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1293

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5123 - loss: 1.1292

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1292

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5122 - loss: 1.1291

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1290

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1290

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1289

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1288

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1288

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1287

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1287

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1286

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5121 - loss: 1.1286

324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5121 - loss: 1.1285

327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5121 - loss: 1.1285

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1285

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1284

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1284

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5120 - loss: 1.1283

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1283

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1283

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1282

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5119 - loss: 1.1282

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5118 - loss: 1.1282

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1282

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1281

369/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5117 - loss: 1.1281

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5116 - loss: 1.1281

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5115 - loss: 1.1281

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5114 - loss: 1.1281

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1280

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5113 - loss: 1.1279

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5112 - loss: 1.1279

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5112 - loss: 1.1279

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1278

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1277

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1277

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1276

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1276

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1275

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1275

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1274

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1274

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5112 - loss: 1.1273

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1273

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1272

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5113 - loss: 1.1272
Epoch 5: val_accuracy did not improve from 0.56701

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5113 - loss: 1.1270 - val_accuracy: 0.5612 - val_loss: 1.0790 - learning_rate: 0.0100
Epoch 6/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:26 183ms/step - accuracy: 0.5000 - loss: 0.9738

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5228 - loss: 1.0491   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5348 - loss: 1.0663

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5303 - loss: 1.0826

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5297 - loss: 1.0888

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5307 - loss: 1.0917

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5329 - loss: 1.0905

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0895

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5361 - loss: 1.0910

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5369 - loss: 1.0918

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5376 - loss: 1.0915

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5383 - loss: 1.0910

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0906

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5392 - loss: 1.0906

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5392 - loss: 1.0905

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5391 - loss: 1.0905

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5388 - loss: 1.0906

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5384 - loss: 1.0909

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5380 - loss: 1.0912

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5375 - loss: 1.0914

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5373 - loss: 1.0916

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5370 - loss: 1.0919

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5367 - loss: 1.0921

 70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5367 - loss: 1.0920

 73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5366 - loss: 1.0920

 76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0919

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0918

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0917

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5365 - loss: 1.0916

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5364 - loss: 1.0916

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5363 - loss: 1.0917

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5362 - loss: 1.0917

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5362 - loss: 1.0918

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0919

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0918

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5360 - loss: 1.0918

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5359 - loss: 1.0920

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5357 - loss: 1.0921

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5355 - loss: 1.0923

118/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5353 - loss: 1.0924

121/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5351 - loss: 1.0926

124/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5349 - loss: 1.0927

127/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5347 - loss: 1.0930

130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5344 - loss: 1.0933

133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5341 - loss: 1.0935

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5339 - loss: 1.0936

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5337 - loss: 1.0939

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5335 - loss: 1.0940

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5333 - loss: 1.0943

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5331 - loss: 1.0945

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5329 - loss: 1.0947

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5326 - loss: 1.0949

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5325 - loss: 1.0951

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5321 - loss: 1.0953

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5319 - loss: 1.0956

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5317 - loss: 1.0958

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5315 - loss: 1.0960

171/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0962

174/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5311 - loss: 1.0963

177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5308 - loss: 1.0965

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5306 - loss: 1.0967

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5303 - loss: 1.0969

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5301 - loss: 1.0972

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5299 - loss: 1.0974

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5297 - loss: 1.0976

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5294 - loss: 1.0978

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5292 - loss: 1.0981

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5290 - loss: 1.0983

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5287 - loss: 1.0984

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5285 - loss: 1.0986

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5283 - loss: 1.0988

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5281 - loss: 1.0989

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5279 - loss: 1.0991

219/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5277 - loss: 1.0993

222/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5275 - loss: 1.0994

225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5273 - loss: 1.0996

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5271 - loss: 1.0997

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5270 - loss: 1.0998

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5268 - loss: 1.0999

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5267 - loss: 1.1001

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5265 - loss: 1.1002

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5264 - loss: 1.1004

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5262 - loss: 1.1006

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5261 - loss: 1.1007

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5260 - loss: 1.1009

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5258 - loss: 1.1010

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5257 - loss: 1.1012

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5255 - loss: 1.1014

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5254 - loss: 1.1016

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5253 - loss: 1.1017

270/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5251 - loss: 1.1019

272/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5250 - loss: 1.1020

275/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5249 - loss: 1.1022

278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5248 - loss: 1.1023

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5247 - loss: 1.1024

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5246 - loss: 1.1026

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5245 - loss: 1.1027

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5244 - loss: 1.1028

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5243 - loss: 1.1029

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5243 - loss: 1.1030

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5242 - loss: 1.1031

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5241 - loss: 1.1031

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5240 - loss: 1.1032

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5240 - loss: 1.1033

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5239 - loss: 1.1034

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5238 - loss: 1.1035

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5237 - loss: 1.1036

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5237 - loss: 1.1037

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5236 - loss: 1.1037

323/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5236 - loss: 1.1038

326/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5235 - loss: 1.1039

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5235 - loss: 1.1039

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5234 - loss: 1.1040

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5234 - loss: 1.1041

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5233 - loss: 1.1042

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1043

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1043

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5232 - loss: 1.1044

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1044

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1045

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5231 - loss: 1.1046

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5230 - loss: 1.1046

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5230 - loss: 1.1046

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1046

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1046

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1047

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5230 - loss: 1.1048

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5230 - loss: 1.1048

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1048

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5231 - loss: 1.1047

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1047

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5232 - loss: 1.1046
Epoch 6: val_accuracy did not improve from 0.56701

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5232 - loss: 1.1046 - val_accuracy: 0.5546 - val_loss: 1.0736 - learning_rate: 0.0100
Epoch 7/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 161ms/step - accuracy: 0.4062 - loss: 1.5171

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4889 - loss: 1.3080   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5020 - loss: 1.2414

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5070 - loss: 1.2142

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5116 - loss: 1.1969

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5150 - loss: 1.1841

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5171 - loss: 1.1746

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5185 - loss: 1.1658

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5189 - loss: 1.1596

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5185 - loss: 1.1549

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5189 - loss: 1.1506

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5194 - loss: 1.1469

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5199 - loss: 1.1435

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5202 - loss: 1.1414

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5207 - loss: 1.1384

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5211 - loss: 1.1356

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414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0921

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0921

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0920

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5329 - loss: 1.0920

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0920

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0919

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0919

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0918

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0917

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0916
Epoch 7: val_accuracy did not improve from 0.56701

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5329 - loss: 1.0916 - val_accuracy: 0.5124 - val_loss: 1.0865 - learning_rate: 0.0100
Epoch 8/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5312 - loss: 1.1688

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5658 - loss: 1.0951   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5644 - loss: 1.0862

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5696 - loss: 1.0738

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5691 - loss: 1.0669

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5684 - loss: 1.0624

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5672 - loss: 1.0624

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5663 - loss: 1.0630

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5652 - loss: 1.0631

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5637 - loss: 1.0639

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5625 - loss: 1.0645

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5605 - loss: 1.0659

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5584 - loss: 1.0675

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5563 - loss: 1.0692

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5541 - loss: 1.0708

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5528 - loss: 1.0719

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5510 - loss: 1.0729

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5493 - loss: 1.0740

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5478 - loss: 1.0751

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5467 - loss: 1.0758

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5457 - loss: 1.0768

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5442 - loss: 1.0780

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5430 - loss: 1.0792

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5420 - loss: 1.0801

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5412 - loss: 1.0808

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5407 - loss: 1.0814

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5402 - loss: 1.0818

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5399 - loss: 1.0820

 81/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5396 - loss: 1.0822

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5395 - loss: 1.0822

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5394 - loss: 1.0823

 90/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5392 - loss: 1.0825

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5391 - loss: 1.0826

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5389 - loss: 1.0827

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5388 - loss: 1.0828

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5387 - loss: 1.0828

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5386 - loss: 1.0829

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5385 - loss: 1.0829

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5385 - loss: 1.0829

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0829

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0829

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5384 - loss: 1.0828

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5383 - loss: 1.0828

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5383 - loss: 1.0829

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0829

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0830

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5382 - loss: 1.0830

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5382 - loss: 1.0830

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0830

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0830

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0831

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0832

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0833

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5384 - loss: 1.0834

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0835

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5383 - loss: 1.0836

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5382 - loss: 1.0837

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5381 - loss: 1.0838

181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5380 - loss: 1.0840

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5379 - loss: 1.0841

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5379 - loss: 1.0842

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5378 - loss: 1.0843

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5377 - loss: 1.0843

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5376 - loss: 1.0844

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5376 - loss: 1.0845

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5375 - loss: 1.0845

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0846

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0847

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0848

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5370 - loss: 1.0850

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0850

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0851

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5367 - loss: 1.0852

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5366 - loss: 1.0853

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0854

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5364 - loss: 1.0855

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0856

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0857

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0858

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0858

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5359 - loss: 1.0859

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5358 - loss: 1.0860

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0860

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0861

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5356 - loss: 1.0861

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5355 - loss: 1.0861

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5355 - loss: 1.0861

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5354 - loss: 1.0862

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5353 - loss: 1.0862

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5353 - loss: 1.0862

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5352 - loss: 1.0862

278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0862

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0862

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0862

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0862

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0862

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5348 - loss: 1.0862

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Epoch 8: val_accuracy improved from 0.56701 to 0.57103, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 9/40
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191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0821

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5374 - loss: 1.0822

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5373 - loss: 1.0822

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0822

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5372 - loss: 1.0823

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0823

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5371 - loss: 1.0823

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5370 - loss: 1.0824

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0824

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5369 - loss: 1.0824

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0825

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5368 - loss: 1.0825

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5367 - loss: 1.0826

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5367 - loss: 1.0826

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5366 - loss: 1.0827

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0828

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0829

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5364 - loss: 1.0829

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0830

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0831

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0831

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0832

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0832

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0833

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5360 - loss: 1.0834

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5359 - loss: 1.0834

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5358 - loss: 1.0835

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0835

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0836

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5356 - loss: 1.0836

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5356 - loss: 1.0837

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5355 - loss: 1.0837

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5355 - loss: 1.0837

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5354 - loss: 1.0838

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5353 - loss: 1.0839

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5353 - loss: 1.0839

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5352 - loss: 1.0840

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5352 - loss: 1.0840

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0841

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5351 - loss: 1.0841

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0841

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0842

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5350 - loss: 1.0842

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0843

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5349 - loss: 1.0843

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5348 - loss: 1.0844

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5348 - loss: 1.0844

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5348 - loss: 1.0845

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5347 - loss: 1.0846

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5347 - loss: 1.0847

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5346 - loss: 1.0847

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5346 - loss: 1.0848

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5345 - loss: 1.0849

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5345 - loss: 1.0850

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0850

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0851

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5344 - loss: 1.0852

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5343 - loss: 1.0853

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5343 - loss: 1.0853

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5342 - loss: 1.0854

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5342 - loss: 1.0855

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5341 - loss: 1.0856

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5341 - loss: 1.0856

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0857

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0858

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5340 - loss: 1.0859

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5339 - loss: 1.0859

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5339 - loss: 1.0860

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5338 - loss: 1.0861

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5337 - loss: 1.0862

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5337 - loss: 1.0863

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0864

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0865

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5336 - loss: 1.0865

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5335 - loss: 1.0866

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5335 - loss: 1.0867

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0867

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0868

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5334 - loss: 1.0869

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5333 - loss: 1.0869

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5333 - loss: 1.0869

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5333 - loss: 1.0870

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0870

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0871

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5332 - loss: 1.0872

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5331 - loss: 1.0872

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5331 - loss: 1.0872

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0873

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0873

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5330 - loss: 1.0874

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0874

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0875

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5329 - loss: 1.0875

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5328 - loss: 1.0875

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5328 - loss: 1.0876
Epoch 9: val_accuracy did not improve from 0.57103

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5327 - loss: 1.0877 - val_accuracy: 0.5433 - val_loss: 1.0395 - learning_rate: 0.0100
Epoch 10/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:28 187ms/step - accuracy: 0.6562 - loss: 0.8568

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5859 - loss: 0.9892   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5549 - loss: 1.0283

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5516 - loss: 1.0330

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5511 - loss: 1.0351

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5498 - loss: 1.0370

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5478 - loss: 1.0400

 21/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5478 - loss: 1.0395

 24/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5479 - loss: 1.0395

 27/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5471 - loss: 1.0398

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5471 - loss: 1.0396

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5475 - loss: 1.0393

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5474 - loss: 1.0394

 39/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5470 - loss: 1.0398

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5468 - loss: 1.0402

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5470 - loss: 1.0401

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5473 - loss: 1.0396

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5476 - loss: 1.0394

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5476 - loss: 1.0396

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5476 - loss: 1.0398

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5477 - loss: 1.0399

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5479 - loss: 1.0400

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5480 - loss: 1.0403

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5482 - loss: 1.0405

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5483 - loss: 1.0407

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Epoch 10: val_accuracy improved from 0.57103 to 0.57344, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 11/40
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331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0855

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5324 - loss: 1.0854

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0854

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0853

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0852

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0851

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0850

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5325 - loss: 1.0850

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0850

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0850

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0849

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5325 - loss: 1.0848

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0847

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5324 - loss: 1.0846

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5324 - loss: 1.0845

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0845

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0845

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0844

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0844

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0843

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0842

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0842

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5325 - loss: 1.0841

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0841

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0840

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0840

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5326 - loss: 1.0839
Epoch 11: val_accuracy did not improve from 0.57344

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5326 - loss: 1.0839 - val_accuracy: 0.5620 - val_loss: 1.0391 - learning_rate: 0.0100
Epoch 12/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.3750 - loss: 1.1347

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4284 - loss: 1.0906   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4428 - loss: 1.0865

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4526 - loss: 1.0836

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4626 - loss: 1.0785

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4729 - loss: 1.0710

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4797 - loss: 1.0666

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4839 - loss: 1.0655

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4873 - loss: 1.0639

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4893 - loss: 1.0625

 30/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4921 - loss: 1.0618

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4950 - loss: 1.0609

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4967 - loss: 1.0604

 38/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.4986 - loss: 1.0604

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5003 - loss: 1.0601

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5021 - loss: 1.0594

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5035 - loss: 1.0588

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5046 - loss: 1.0589

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5057 - loss: 1.0592

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5065 - loss: 1.0593

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5075 - loss: 1.0593

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5084 - loss: 1.0595

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5092 - loss: 1.0599

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5099 - loss: 1.0602

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5106 - loss: 1.0607

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5112 - loss: 1.0613

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5118 - loss: 1.0619

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5124 - loss: 1.0623

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5131 - loss: 1.0627

 84/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5135 - loss: 1.0630

 87/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5142 - loss: 1.0634

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5147 - loss: 1.0639

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5153 - loss: 1.0643

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5158 - loss: 1.0648

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5162 - loss: 1.0652

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5167 - loss: 1.0656

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5170 - loss: 1.0659

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5174 - loss: 1.0662

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5177 - loss: 1.0666

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5179 - loss: 1.0669

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5182 - loss: 1.0672

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5184 - loss: 1.0674

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5186 - loss: 1.0676

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5189 - loss: 1.0677

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5190 - loss: 1.0678

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5192 - loss: 1.0680

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5193 - loss: 1.0682

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5195 - loss: 1.0684

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5196 - loss: 1.0687

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5197 - loss: 1.0689

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5199 - loss: 1.0691

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5199 - loss: 1.0694

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5200 - loss: 1.0697

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5201 - loss: 1.0700

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5201 - loss: 1.0702

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5202 - loss: 1.0705

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5203 - loss: 1.0707

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5203 - loss: 1.0710

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5204 - loss: 1.0713

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5204 - loss: 1.0717

175/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5204 - loss: 1.0720

178/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5205 - loss: 1.0723

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5205 - loss: 1.0725

182/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5206 - loss: 1.0727

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5206 - loss: 1.0729

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5207 - loss: 1.0732

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5207 - loss: 1.0734

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5207 - loss: 1.0737

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5208 - loss: 1.0740

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5208 - loss: 1.0743

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5208 - loss: 1.0746

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5209 - loss: 1.0748

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5209 - loss: 1.0750

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Epoch 12: val_accuracy improved from 0.57344 to 0.57685, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 13/40
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Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.
Epoch 13: val_accuracy did not improve from 0.57685

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5457 - loss: 1.0763 - val_accuracy: 0.5668 - val_loss: 1.0359 - learning_rate: 0.0100
Epoch 14/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.4062 - loss: 1.3111

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4375 - loss: 1.2257   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4524 - loss: 1.2047

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.4663 - loss: 1.1860

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.4752 - loss: 1.1755

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.4832 - loss: 1.1660

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4891 - loss: 1.1590

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4940 - loss: 1.1518

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4978 - loss: 1.1452

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5012 - loss: 1.1388

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5039 - loss: 1.1337

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5059 - loss: 1.1300

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5077 - loss: 1.1266

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5093 - loss: 1.1234

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5106 - loss: 1.1208

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5118 - loss: 1.1188

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5126 - loss: 1.1175

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5132 - loss: 1.1164

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5138 - loss: 1.1155

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5144 - loss: 1.1147

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5150 - loss: 1.1139

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5155 - loss: 1.1130

 67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5159 - loss: 1.1123

 70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5163 - loss: 1.1117

 73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5167 - loss: 1.1111

 76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5171 - loss: 1.1104

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5176 - loss: 1.1097

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5181 - loss: 1.1090

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5185 - loss: 1.1083

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5189 - loss: 1.1075

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5194 - loss: 1.1067

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5199 - loss: 1.1059

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5204 - loss: 1.1051

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5207 - loss: 1.1046

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5211 - loss: 1.1041

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5216 - loss: 1.1034

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5221 - loss: 1.1028

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5226 - loss: 1.1021

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5231 - loss: 1.1014

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5235 - loss: 1.1008

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5240 - loss: 1.1001

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5245 - loss: 1.0995

125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5249 - loss: 1.0989

128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5254 - loss: 1.0982

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5258 - loss: 1.0976

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5263 - loss: 1.0969

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5267 - loss: 1.0963

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5271 - loss: 1.0956

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5275 - loss: 1.0950

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5279 - loss: 1.0945

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5283 - loss: 1.0940

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5287 - loss: 1.0935

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5291 - loss: 1.0930

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5294 - loss: 1.0925

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5298 - loss: 1.0920

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5301 - loss: 1.0916

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5304 - loss: 1.0912

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5307 - loss: 1.0908

173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5310 - loss: 1.0904

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5313 - loss: 1.0900

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5316 - loss: 1.0896

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5318 - loss: 1.0893

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5321 - loss: 1.0890

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5322 - loss: 1.0887

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5325 - loss: 1.0884

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5327 - loss: 1.0880

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5330 - loss: 1.0877

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5332 - loss: 1.0873

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5335 - loss: 1.0870

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5337 - loss: 1.0866

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5339 - loss: 1.0863

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5341 - loss: 1.0859

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5344 - loss: 1.0856

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5346 - loss: 1.0852

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5348 - loss: 1.0849

223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5351 - loss: 1.0846

226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5353 - loss: 1.0842

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5355 - loss: 1.0839

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5357 - loss: 1.0836

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5359 - loss: 1.0833

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5361 - loss: 1.0830

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5362 - loss: 1.0828

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5363 - loss: 1.0826

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5365 - loss: 1.0825

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5366 - loss: 1.0823

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5367 - loss: 1.0821

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5368 - loss: 1.0819

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5370 - loss: 1.0817

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5372 - loss: 1.0814

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5374 - loss: 1.0811

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5376 - loss: 1.0808

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5377 - loss: 1.0806

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5379 - loss: 1.0803

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5381 - loss: 1.0800

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5382 - loss: 1.0798

277/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5384 - loss: 1.0796

280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5386 - loss: 1.0793

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5387 - loss: 1.0792

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5388 - loss: 1.0790

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5389 - loss: 1.0788

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5391 - loss: 1.0785

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5392 - loss: 1.0783

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5393 - loss: 1.0780

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5395 - loss: 1.0778

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5396 - loss: 1.0776

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5397 - loss: 1.0774

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5399 - loss: 1.0772

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5400 - loss: 1.0769

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317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5402 - loss: 1.0765

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328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5407 - loss: 1.0757

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Epoch 14: val_accuracy improved from 0.57685 to 0.58107, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 15/40
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239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5602 - loss: 1.0198

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0198

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0197

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0197

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5603 - loss: 1.0196

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5604 - loss: 1.0196

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5604 - loss: 1.0196

275/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195

278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5604 - loss: 1.0195

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0194

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5603 - loss: 1.0195

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5602 - loss: 1.0195

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5602 - loss: 1.0195

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0196

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0196

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0196

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0197

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0197

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0198

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0199

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0199

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0200

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0200

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0201

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0201

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0202

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0202

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0203

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0203

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0204

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0204

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5597 - loss: 1.0205

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0205

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0206

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0206

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5596 - loss: 1.0207

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0207

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0208

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0208

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5595 - loss: 1.0209

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0209

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0210

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0210

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0211

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5594 - loss: 1.0211

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0212

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0212

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0213

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0213

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0214

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5593 - loss: 1.0214

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0215

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0215

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0216

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0216

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0217

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0217

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0218

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0218

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0219
Epoch 15: val_accuracy did not improve from 0.58107

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5591 - loss: 1.0220 - val_accuracy: 0.5773 - val_loss: 0.9963 - learning_rate: 0.0020
Epoch 16/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:18 166ms/step - accuracy: 0.5000 - loss: 1.1545

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5013 - loss: 1.0755   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5167 - loss: 1.0549

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5265 - loss: 1.0448

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5331 - loss: 1.0373

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5363 - loss: 1.0355

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5380 - loss: 1.0355

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5402 - loss: 1.0352

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5422 - loss: 1.0343

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5430 - loss: 1.0345

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5433 - loss: 1.0344

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5434 - loss: 1.0340

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5437 - loss: 1.0334

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0330

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5442 - loss: 1.0328

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5443 - loss: 1.0331

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5445 - loss: 1.0333

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5447 - loss: 1.0334

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5451 - loss: 1.0336

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0337

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5457 - loss: 1.0337

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5458 - loss: 1.0340

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5459 - loss: 1.0343

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5460 - loss: 1.0345

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5461 - loss: 1.0347

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5462 - loss: 1.0350

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5464 - loss: 1.0352

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5467 - loss: 1.0352

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5470 - loss: 1.0351

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5474 - loss: 1.0350

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5477 - loss: 1.0350

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5480 - loss: 1.0350

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5482 - loss: 1.0350

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5485 - loss: 1.0350

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5487 - loss: 1.0350

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5489 - loss: 1.0350

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5491 - loss: 1.0351

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5493 - loss: 1.0351

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5494 - loss: 1.0352

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5495 - loss: 1.0353

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5496 - loss: 1.0355

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5497 - loss: 1.0355

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Epoch 16: val_accuracy improved from 0.58107 to 0.58449, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5574 - loss: 1.0329 - val_accuracy: 0.5845 - val_loss: 0.9837 - learning_rate: 0.0020
Epoch 17/40
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Epoch 17: val_accuracy improved from 0.58449 to 0.58590, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5627 - loss: 1.0313 - val_accuracy: 0.5859 - val_loss: 0.9805 - learning_rate: 0.0020
Epoch 18/40
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Epoch 18: val_accuracy improved from 0.58590 to 0.58991, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5648 - loss: 1.0168 - val_accuracy: 0.5899 - val_loss: 0.9764 - learning_rate: 0.0020
Epoch 19/40
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137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5768 - loss: 1.0136

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5765 - loss: 1.0139

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 1.0142

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5759 - loss: 1.0145

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5757 - loss: 1.0147

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5754 - loss: 1.0150

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5752 - loss: 1.0152

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5750 - loss: 1.0154

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5748 - loss: 1.0157

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5746 - loss: 1.0159

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5744 - loss: 1.0162

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5741 - loss: 1.0164

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5739 - loss: 1.0166

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5738 - loss: 1.0168

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5736 - loss: 1.0169

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5734 - loss: 1.0172

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5732 - loss: 1.0174

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5731 - loss: 1.0175

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5729 - loss: 1.0177

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0179

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0180

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 1.0182

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 1.0184

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5720 - loss: 1.0185

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5718 - loss: 1.0187

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 1.0188

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5716 - loss: 1.0190

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5714 - loss: 1.0192

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5712 - loss: 1.0193

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5711 - loss: 1.0195

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5710 - loss: 1.0196

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5709 - loss: 1.0197

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5708 - loss: 1.0198

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5707 - loss: 1.0199

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5706 - loss: 1.0199

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5705 - loss: 1.0200

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5704 - loss: 1.0201

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5703 - loss: 1.0202

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0203

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0204

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5700 - loss: 1.0205

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5699 - loss: 1.0205

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5698 - loss: 1.0206

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0206

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5697 - loss: 1.0207

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5696 - loss: 1.0207

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0208

276/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5695 - loss: 1.0208

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5694 - loss: 1.0208

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5693 - loss: 1.0209

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5693 - loss: 1.0209

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0210

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0210

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5691 - loss: 1.0210

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5691 - loss: 1.0210

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5690 - loss: 1.0211

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5690 - loss: 1.0211

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0211

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0211

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5688 - loss: 1.0212

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5688 - loss: 1.0212

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5687 - loss: 1.0212

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5687 - loss: 1.0212

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5686 - loss: 1.0212

325/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5686 - loss: 1.0212

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5685 - loss: 1.0213

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5685 - loss: 1.0213

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5685 - loss: 1.0213

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0214

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0214

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0214

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5683 - loss: 1.0214

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5683 - loss: 1.0214

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5682 - loss: 1.0215

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5682 - loss: 1.0215

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5682 - loss: 1.0215

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5682 - loss: 1.0215

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5681 - loss: 1.0216

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5681 - loss: 1.0216

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0216

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5680 - loss: 1.0217

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0217

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5679 - loss: 1.0218

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5678 - loss: 1.0218

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5678 - loss: 1.0219

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5677 - loss: 1.0219

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5677 - loss: 1.0220

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5676 - loss: 1.0220

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5676 - loss: 1.0221

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5675 - loss: 1.0221

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5675 - loss: 1.0221

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5675 - loss: 1.0222

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5674 - loss: 1.0222

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5674 - loss: 1.0222

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5673 - loss: 1.0223

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5673 - loss: 1.0223

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5673 - loss: 1.0223

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5673 - loss: 1.0223

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5672 - loss: 1.0224

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5672 - loss: 1.0224

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5672 - loss: 1.0224

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5671 - loss: 1.0224

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5671 - loss: 1.0224

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5671 - loss: 1.0225

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5670 - loss: 1.0225

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5670 - loss: 1.0225

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5670 - loss: 1.0225

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0225

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0226

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5669 - loss: 1.0226

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0226

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0227

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5668 - loss: 1.0227

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5667 - loss: 1.0228
Epoch 19: val_accuracy did not improve from 0.58991

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5666 - loss: 1.0228 - val_accuracy: 0.5811 - val_loss: 0.9902 - learning_rate: 0.0020
Epoch 20/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 161ms/step - accuracy: 0.4688 - loss: 1.0892

  4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.4674 - loss: 1.1093  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4852 - loss: 1.0993 

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4878 - loss: 1.0971

 12/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.4934 - loss: 1.0912

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.4988 - loss: 1.0879

 17/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5017 - loss: 1.0868

 20/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5055 - loss: 1.0840

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380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0366

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0365

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5537 - loss: 1.0365

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0364

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5538 - loss: 1.0364

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0363

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5539 - loss: 1.0362

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0362

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0361

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5540 - loss: 1.0361

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0360

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0360

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5541 - loss: 1.0359

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5542 - loss: 1.0359

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5542 - loss: 1.0358

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0358

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0357

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5543 - loss: 1.0356

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0356

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5544 - loss: 1.0355

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0355

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5545 - loss: 1.0354

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0353

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0353

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5546 - loss: 1.0352

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0352

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0351

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5547 - loss: 1.0351

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5548 - loss: 1.0350
Epoch 20: val_accuracy did not improve from 0.58991

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5548 - loss: 1.0350 - val_accuracy: 0.5897 - val_loss: 0.9865 - learning_rate: 0.0020
Epoch 21/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5938 - loss: 0.9371

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5807 - loss: 0.9520   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5570 - loss: 0.9910

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5446 - loss: 1.0062

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5385 - loss: 1.0189

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0264

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5350 - loss: 1.0288

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5353 - loss: 1.0295

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5350 - loss: 1.0303

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5352 - loss: 1.0311

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5360 - loss: 1.0307

 33/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5366 - loss: 1.0302

 35/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5376 - loss: 1.0294

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5387 - loss: 1.0285

 41/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5398 - loss: 1.0280

 44/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5409 - loss: 1.0274

 47/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5424 - loss: 1.0265

 50/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5440 - loss: 1.0253

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5454 - loss: 1.0242

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5469 - loss: 1.0229

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5483 - loss: 1.0219

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5495 - loss: 1.0210

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5506 - loss: 1.0201

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5516 - loss: 1.0192

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5526 - loss: 1.0184

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5535 - loss: 1.0176

 77/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5544 - loss: 1.0167

 80/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5552 - loss: 1.0160

 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5559 - loss: 1.0155

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5566 - loss: 1.0151

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5572 - loss: 1.0147

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5578 - loss: 1.0145

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5585 - loss: 1.0143

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5590 - loss: 1.0141

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5594 - loss: 1.0141

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5598 - loss: 1.0140

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5602 - loss: 1.0139

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5606 - loss: 1.0138

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5610 - loss: 1.0137

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5613 - loss: 1.0136

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5616 - loss: 1.0136

122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0136

125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5620 - loss: 1.0136

128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5621 - loss: 1.0138

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5623 - loss: 1.0138

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5625 - loss: 1.0139

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5626 - loss: 1.0140

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5628 - loss: 1.0141

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5629 - loss: 1.0142

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5631 - loss: 1.0142

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5632 - loss: 1.0144

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5633 - loss: 1.0145

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5635 - loss: 1.0146

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5636 - loss: 1.0147

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5637 - loss: 1.0148

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5638 - loss: 1.0149

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5639 - loss: 1.0151

170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5640 - loss: 1.0152

173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5641 - loss: 1.0153

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5642 - loss: 1.0154

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5643 - loss: 1.0156

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0157

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5644 - loss: 1.0159

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0160

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0161

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5645 - loss: 1.0163

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5646 - loss: 1.0164

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0165

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0166

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5647 - loss: 1.0167

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5648 - loss: 1.0168

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5648 - loss: 1.0169

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5648 - loss: 1.0170

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5649 - loss: 1.0171

221/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5649 - loss: 1.0171

223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5650 - loss: 1.0172

226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5650 - loss: 1.0173

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5650 - loss: 1.0173

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5651 - loss: 1.0174

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5651 - loss: 1.0175

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5652 - loss: 1.0175

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5652 - loss: 1.0176

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5653 - loss: 1.0176

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5653 - loss: 1.0177

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5654 - loss: 1.0178

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5654 - loss: 1.0178

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5654 - loss: 1.0179

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5655 - loss: 1.0180

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5655 - loss: 1.0180

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Epoch 21: val_accuracy improved from 0.58991 to 0.59011, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 22/40
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156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0468

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0466

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0465

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0464

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0462

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0461

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0459

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0457

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0455

181/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5557 - loss: 1.0454

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0452

186/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5558 - loss: 1.0450

189/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5559 - loss: 1.0447

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5559 - loss: 1.0445

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5560 - loss: 1.0442

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5560 - loss: 1.0440

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5561 - loss: 1.0438

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5561 - loss: 1.0437

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5561 - loss: 1.0435

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5562 - loss: 1.0433

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5562 - loss: 1.0430

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5563 - loss: 1.0428

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5564 - loss: 1.0426

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5564 - loss: 1.0424

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5565 - loss: 1.0421

228/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5566 - loss: 1.0419

231/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5566 - loss: 1.0417

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5567 - loss: 1.0414

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5568 - loss: 1.0412

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5568 - loss: 1.0409

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5569 - loss: 1.0407

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5570 - loss: 1.0404

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5570 - loss: 1.0402

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5571 - loss: 1.0400

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5571 - loss: 1.0398

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5572 - loss: 1.0396

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5572 - loss: 1.0393

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5573 - loss: 1.0391

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5573 - loss: 1.0389

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0387

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0385

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5574 - loss: 1.0384

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5575 - loss: 1.0382

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5575 - loss: 1.0380

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0379

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5576 - loss: 1.0377

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5577 - loss: 1.0376

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5577 - loss: 1.0374

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0373

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0371

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5578 - loss: 1.0370

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0369

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0368

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5579 - loss: 1.0367

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0365

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0364

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5580 - loss: 1.0363

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0362

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5581 - loss: 1.0361

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5581 - loss: 1.0360

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0359

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0358

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0357

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5582 - loss: 1.0356

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0355

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5583 - loss: 1.0354

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5583 - loss: 1.0353

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0352

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0351

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5584 - loss: 1.0350

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0349

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0348

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5585 - loss: 1.0347

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5586 - loss: 1.0346

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5586 - loss: 1.0345

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5586 - loss: 1.0344

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0344

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0343

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0342

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0342

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0341

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5587 - loss: 1.0340

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0340

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0339

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0339

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0338

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0337

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5588 - loss: 1.0337

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0336

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0335

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0334

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5589 - loss: 1.0334

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5589 - loss: 1.0333

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0332

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0331

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0330

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5590 - loss: 1.0329

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0328

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0327

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0327

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0326

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5591 - loss: 1.0325

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0324

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0323

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0323

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5592 - loss: 1.0322
Epoch 22: val_accuracy did not improve from 0.59011

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5593 - loss: 1.0319 - val_accuracy: 0.5881 - val_loss: 0.9844 - learning_rate: 0.0020
Epoch 23/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.6562 - loss: 0.8490

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6204 - loss: 0.8606   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6149 - loss: 0.8746

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6133 - loss: 0.8882

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6104 - loss: 0.9031

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6070 - loss: 0.9169

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6054 - loss: 0.9257

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6038 - loss: 0.9326

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6022 - loss: 0.9379

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6009 - loss: 0.9416

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5995 - loss: 0.9455

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5981 - loss: 0.9488

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5965 - loss: 0.9517

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5959 - loss: 0.9535

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419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5734 - loss: 1.0070

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5733 - loss: 1.0071

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5732 - loss: 1.0072

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5731 - loss: 1.0074

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5731 - loss: 1.0075

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5730 - loss: 1.0076

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5729 - loss: 1.0077

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5729 - loss: 1.0078

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0080

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0081

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0082

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0083

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0084

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0085

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0086

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0088

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0089
Epoch 23: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.
Epoch 23: val_accuracy did not improve from 0.59011

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5721 - loss: 1.0091 - val_accuracy: 0.5887 - val_loss: 0.9781 - learning_rate: 0.0020
Epoch 24/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 160ms/step - accuracy: 0.5625 - loss: 1.0324

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5436 - loss: 1.0611   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5478 - loss: 1.0466

  9/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5470 - loss: 1.0453

 12/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5496 - loss: 1.0433

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5497 - loss: 1.0425 

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5491 - loss: 1.0430

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5481 - loss: 1.0434

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5470 - loss: 1.0444

 26/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5465 - loss: 1.0447

 29/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5459 - loss: 1.0456

 31/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5454 - loss: 1.0464

 34/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5453 - loss: 1.0473

 37/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5452 - loss: 1.0483

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5451 - loss: 1.0494

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5452 - loss: 1.0499

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5456 - loss: 1.0498

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5459 - loss: 1.0496

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5461 - loss: 1.0496

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5464 - loss: 1.0495

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5467 - loss: 1.0493

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5470 - loss: 1.0491

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5471 - loss: 1.0490

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5474 - loss: 1.0486

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5475 - loss: 1.0484

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5477 - loss: 1.0482

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5479 - loss: 1.0478

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5481 - loss: 1.0476

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5484 - loss: 1.0472

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5485 - loss: 1.0469

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5487 - loss: 1.0466

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5488 - loss: 1.0462

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5490 - loss: 1.0460

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5492 - loss: 1.0457

 95/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5494 - loss: 1.0454

 98/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5495 - loss: 1.0452

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5496 - loss: 1.0450

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5497 - loss: 1.0448

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5498 - loss: 1.0445

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5500 - loss: 1.0443

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5501 - loss: 1.0441

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5502 - loss: 1.0439

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5503 - loss: 1.0438

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5504 - loss: 1.0437

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5505 - loss: 1.0436

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5506 - loss: 1.0436

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5506 - loss: 1.0436

132/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5507 - loss: 1.0435

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435

136/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435

139/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5508 - loss: 1.0435

142/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5509 - loss: 1.0434

145/473 ━━━━━━━━━━━━━━━━━━━━ 7s 22ms/step - accuracy: 0.5510 - loss: 1.0433

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5511 - loss: 1.0432

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5512 - loss: 1.0430

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5514 - loss: 1.0429

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5515 - loss: 1.0427

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5517 - loss: 1.0425

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5519 - loss: 1.0423

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5520 - loss: 1.0422

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5521 - loss: 1.0420

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5522 - loss: 1.0418

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5523 - loss: 1.0416

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5525 - loss: 1.0415

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5526 - loss: 1.0413

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5528 - loss: 1.0410

185/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5528 - loss: 1.0409

187/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5529 - loss: 1.0408

190/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5530 - loss: 1.0406

193/473 ━━━━━━━━━━━━━━━━━━━━ 6s 22ms/step - accuracy: 0.5532 - loss: 1.0404

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5533 - loss: 1.0403

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5534 - loss: 1.0401

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5534 - loss: 1.0400

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5535 - loss: 1.0398

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5536 - loss: 1.0397

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5537 - loss: 1.0396

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5538 - loss: 1.0395

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5539 - loss: 1.0393

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5540 - loss: 1.0392

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5540 - loss: 1.0391

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5541 - loss: 1.0390

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5542 - loss: 1.0389

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5543 - loss: 1.0388

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5544 - loss: 1.0386

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5545 - loss: 1.0385

239/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5546 - loss: 1.0384

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5546 - loss: 1.0383

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5547 - loss: 1.0382

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5547 - loss: 1.0381

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5548 - loss: 1.0380

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5549 - loss: 1.0379

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5550 - loss: 1.0378

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5550 - loss: 1.0377

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5551 - loss: 1.0376

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5551 - loss: 1.0375

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - accuracy: 0.5552 - loss: 1.0374

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5553 - loss: 1.0373

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5553 - loss: 1.0372

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5554 - loss: 1.0371

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5554 - loss: 1.0370

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5555 - loss: 1.0370

284/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5555 - loss: 1.0369

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Epoch 24: val_accuracy improved from 0.59011 to 0.59052, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5575 - loss: 1.0339 - val_accuracy: 0.5905 - val_loss: 0.9716 - learning_rate: 4.0000e-04
Epoch 25/40
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Epoch 25: val_accuracy improved from 0.59052 to 0.59072, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5713 - loss: 1.0176 - val_accuracy: 0.5907 - val_loss: 0.9717 - learning_rate: 4.0000e-04
Epoch 26/40
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426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5708 - loss: 1.0164

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5707 - loss: 1.0164

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5706 - loss: 1.0165

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5705 - loss: 1.0165
Epoch 26: val_accuracy did not improve from 0.59072

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5704 - loss: 1.0166 - val_accuracy: 0.5895 - val_loss: 0.9729 - learning_rate: 4.0000e-04
Epoch 27/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.5938 - loss: 1.0087

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6302 - loss: 0.9698   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6390 - loss: 0.9481

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6337 - loss: 0.9477

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6335 - loss: 0.9418

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6315 - loss: 0.9424

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6279 - loss: 0.9459

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6247 - loss: 0.9489

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6215 - loss: 0.9527

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6192 - loss: 0.9554

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6172 - loss: 0.9581

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6156 - loss: 0.9605

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6134 - loss: 0.9633

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6112 - loss: 0.9662

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6092 - loss: 0.9690

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6076 - loss: 0.9711

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6058 - loss: 0.9734

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6047 - loss: 0.9750

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6033 - loss: 0.9770

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.6019 - loss: 0.9788

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6007 - loss: 0.9803

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5997 - loss: 0.9815

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5988 - loss: 0.9827

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5978 - loss: 0.9839

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5968 - loss: 0.9850

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5958 - loss: 0.9862

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5951 - loss: 0.9869

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5942 - loss: 0.9881

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5933 - loss: 0.9891

 86/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5924 - loss: 0.9901

 89/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5916 - loss: 0.9910

 92/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5908 - loss: 0.9921

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5901 - loss: 0.9930

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5894 - loss: 0.9939

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5888 - loss: 0.9948

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5882 - loss: 0.9956

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5877 - loss: 0.9963

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5872 - loss: 0.9969

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5869 - loss: 0.9974

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5865 - loss: 0.9980

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5861 - loss: 0.9985

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5859 - loss: 0.9987

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5856 - loss: 0.9991

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5853 - loss: 0.9995

130/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5851 - loss: 0.9998

133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5849 - loss: 1.0000

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5848 - loss: 1.0001

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5847 - loss: 1.0003

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5845 - loss: 1.0004

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5844 - loss: 1.0005

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5842 - loss: 1.0006

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5841 - loss: 1.0007

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 1.0008

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 1.0009

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5839 - loss: 1.0010

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5838 - loss: 1.0011

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5837 - loss: 1.0012

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5835 - loss: 1.0013

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5834 - loss: 1.0014

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5833 - loss: 1.0016

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5831 - loss: 1.0017

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5830 - loss: 1.0018

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5829 - loss: 1.0019

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5827 - loss: 1.0021

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5826 - loss: 1.0022

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5824 - loss: 1.0024

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5822 - loss: 1.0026

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5820 - loss: 1.0028

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5819 - loss: 1.0029

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5817 - loss: 1.0031

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5815 - loss: 1.0033

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5813 - loss: 1.0034

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5811 - loss: 1.0036

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5809 - loss: 1.0038

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5807 - loss: 1.0040

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5806 - loss: 1.0042

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5804 - loss: 1.0043

226/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5802 - loss: 1.0045

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5800 - loss: 1.0047

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5798 - loss: 1.0049

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5797 - loss: 1.0051

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5795 - loss: 1.0052

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5794 - loss: 1.0053

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5792 - loss: 1.0054

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5791 - loss: 1.0056

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5788 - loss: 1.0058

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5787 - loss: 1.0060

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5785 - loss: 1.0061

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5784 - loss: 1.0062

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5783 - loss: 1.0064

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5781 - loss: 1.0065

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5780 - loss: 1.0066

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5778 - loss: 1.0067

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5777 - loss: 1.0069

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5775 - loss: 1.0070

278/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5774 - loss: 1.0071

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5773 - loss: 1.0072

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5771 - loss: 1.0073

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5770 - loss: 1.0074

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5769 - loss: 1.0075

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5767 - loss: 1.0076

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5767 - loss: 1.0077

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5765 - loss: 1.0078

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5764 - loss: 1.0079

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5763 - loss: 1.0080

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Epoch 27: val_accuracy improved from 0.59072 to 0.59092, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5723 - loss: 1.0111 - val_accuracy: 0.5909 - val_loss: 0.9703 - learning_rate: 4.0000e-04
Epoch 28/40
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Epoch 28: val_accuracy improved from 0.59092 to 0.59273, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_efficient_20240411-002709.keras

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Epoch 29/40
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441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 1.0085

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 1.0085

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 1.0085

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 1.0085

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0085

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0086

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 1.0086
Epoch 29: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.5757 - loss: 1.0086 - val_accuracy: 0.5913 - val_loss: 0.9718 - learning_rate: 4.0000e-04
Epoch 30/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5312 - loss: 1.2266

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5156 - loss: 1.1374   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5179 - loss: 1.1166

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5214 - loss: 1.0958

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5278 - loss: 1.0755

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5332 - loss: 1.0597

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5365 - loss: 1.0495

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5388 - loss: 1.0434

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5400 - loss: 1.0397

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5415 - loss: 1.0358

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5429 - loss: 1.0325

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5440 - loss: 1.0296

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5447 - loss: 1.0276

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5453 - loss: 1.0260

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5457 - loss: 1.0249

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5458 - loss: 1.0242

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5460 - loss: 1.0239

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5461 - loss: 1.0237

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5459 - loss: 1.0239

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5458 - loss: 1.0241

 61/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5458 - loss: 1.0241

 64/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5460 - loss: 1.0240

 67/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5460 - loss: 1.0242

 70/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5462 - loss: 1.0241

 73/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5466 - loss: 1.0240

 76/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5470 - loss: 1.0238

 79/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5475 - loss: 1.0236

 82/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5480 - loss: 1.0233

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5484 - loss: 1.0232

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5489 - loss: 1.0229

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5493 - loss: 1.0227

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5496 - loss: 1.0225

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5499 - loss: 1.0224

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5501 - loss: 1.0223

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5503 - loss: 1.0221

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5505 - loss: 1.0220

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5507 - loss: 1.0219

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5510 - loss: 1.0218

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5512 - loss: 1.0218

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5515 - loss: 1.0217

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5516 - loss: 1.0218

122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5517 - loss: 1.0219

125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5518 - loss: 1.0219

128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5520 - loss: 1.0219

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5522 - loss: 1.0219

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5524 - loss: 1.0218

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5526 - loss: 1.0218

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5527 - loss: 1.0218

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5528 - loss: 1.0218

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5529 - loss: 1.0218

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5530 - loss: 1.0218

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5532 - loss: 1.0219

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5533 - loss: 1.0219

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5534 - loss: 1.0219

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5535 - loss: 1.0219

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5536 - loss: 1.0219

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5536 - loss: 1.0219

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5537 - loss: 1.0219

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5538 - loss: 1.0219

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5538 - loss: 1.0218

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5539 - loss: 1.0218

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5540 - loss: 1.0218

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5541 - loss: 1.0217

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5542 - loss: 1.0217

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5544 - loss: 1.0216

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5545 - loss: 1.0215

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5547 - loss: 1.0214

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5548 - loss: 1.0213

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5549 - loss: 1.0212

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5551 - loss: 1.0211

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5552 - loss: 1.0210

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5553 - loss: 1.0210

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5554 - loss: 1.0209

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5554 - loss: 1.0208

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5555 - loss: 1.0208

224/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5556 - loss: 1.0207

227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5557 - loss: 1.0207

230/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5558 - loss: 1.0206

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5559 - loss: 1.0205

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5560 - loss: 1.0204

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5561 - loss: 1.0203

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5562 - loss: 1.0201

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5563 - loss: 1.0200

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5564 - loss: 1.0199

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5566 - loss: 1.0197

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5567 - loss: 1.0196

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5568 - loss: 1.0195

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5569 - loss: 1.0193

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5570 - loss: 1.0192

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5571 - loss: 1.0191

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5573 - loss: 1.0190

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5574 - loss: 1.0188

274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5575 - loss: 1.0187

277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5576 - loss: 1.0186

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5577 - loss: 1.0185

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5578 - loss: 1.0184

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5579 - loss: 1.0183

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5581 - loss: 1.0182

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5582 - loss: 1.0181

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5583 - loss: 1.0180

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5584 - loss: 1.0179

301/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5585 - loss: 1.0178

304/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5586 - loss: 1.0177

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5587 - loss: 1.0176

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5588 - loss: 1.0176

310/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5589 - loss: 1.0175

313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5590 - loss: 1.0174

316/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5591 - loss: 1.0174

319/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5592 - loss: 1.0173

322/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5593 - loss: 1.0172

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5594 - loss: 1.0171

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5595 - loss: 1.0171

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5596 - loss: 1.0170

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5596 - loss: 1.0170

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5597 - loss: 1.0169

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5598 - loss: 1.0169

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0168

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5599 - loss: 1.0168

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5600 - loss: 1.0167

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5601 - loss: 1.0167

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0166

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5602 - loss: 1.0166

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5603 - loss: 1.0166

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5604 - loss: 1.0165

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5604 - loss: 1.0165

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5605 - loss: 1.0164

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5605 - loss: 1.0164

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5606 - loss: 1.0164

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5606 - loss: 1.0163

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5607 - loss: 1.0163

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5608 - loss: 1.0163

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5608 - loss: 1.0162

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5609 - loss: 1.0162

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5609 - loss: 1.0162

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5610 - loss: 1.0161

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5610 - loss: 1.0161

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5611 - loss: 1.0161

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5611 - loss: 1.0160

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5612 - loss: 1.0160

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5613 - loss: 1.0160

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5613 - loss: 1.0159

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5614 - loss: 1.0159

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5614 - loss: 1.0159

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5615 - loss: 1.0158

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5616 - loss: 1.0158

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5617 - loss: 1.0157

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5617 - loss: 1.0157

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5618 - loss: 1.0157

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5618 - loss: 1.0156

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0156

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5620 - loss: 1.0155

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5620 - loss: 1.0155

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5621 - loss: 1.0154

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5621 - loss: 1.0154

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5622 - loss: 1.0154

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5622 - loss: 1.0153
Epoch 30: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5623 - loss: 1.0153 - val_accuracy: 0.5927 - val_loss: 0.9712 - learning_rate: 4.0000e-04
Epoch 31/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 159ms/step - accuracy: 0.5312 - loss: 0.9312

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5853 - loss: 0.9403   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5871 - loss: 0.9412

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5914 - loss: 0.9322

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5934 - loss: 0.9309

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5925 - loss: 0.9343

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5926 - loss: 0.9369

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5918 - loss: 0.9392

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5894 - loss: 0.9427

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5877 - loss: 0.9461

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5855 - loss: 0.9502

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5843 - loss: 0.9533

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5834 - loss: 0.9558

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5828 - loss: 0.9582

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5822 - loss: 0.9600

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5817 - loss: 0.9619

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5812 - loss: 0.9637

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5810 - loss: 0.9650

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5808 - loss: 0.9662

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5806 - loss: 0.9675

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5801 - loss: 0.9690

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5798 - loss: 0.9699

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5795 - loss: 0.9707

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5792 - loss: 0.9717

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5789 - loss: 0.9728

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5785 - loss: 0.9737

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5783 - loss: 0.9742

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 22ms/step - accuracy: 0.5781 - loss: 0.9747

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5779 - loss: 0.9753

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5777 - loss: 0.9760

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5776 - loss: 0.9766

 89/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5774 - loss: 0.9771

 92/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5772 - loss: 0.9777

 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9781

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5770 - loss: 0.9785

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9789

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9793

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5766 - loss: 0.9797

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9802

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 0.9805

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5763 - loss: 0.9809

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5762 - loss: 0.9812

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9815

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9818

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9820

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5761 - loss: 0.9822

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5762 - loss: 0.9823

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5761 - loss: 0.9825

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5761 - loss: 0.9828

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5760 - loss: 0.9830

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5759 - loss: 0.9833

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5758 - loss: 0.9835

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5758 - loss: 0.9836

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5757 - loss: 0.9838

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5756 - loss: 0.9840

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5755 - loss: 0.9842

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5754 - loss: 0.9843

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9844

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9845

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5752 - loss: 0.9846

177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5751 - loss: 0.9847

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5751 - loss: 0.9848

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9849

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9850

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9850

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9851

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9852

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9853

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9854

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9854

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5749 - loss: 0.9855

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9856

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9857

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9857

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9858

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5750 - loss: 0.9859

225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9860

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9861

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9863

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9864

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5750 - loss: 0.9865

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9866

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9867

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9868

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9869

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9870

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9871

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9872

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9873

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9874

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9875

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5751 - loss: 0.9875

273/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9876

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9877

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9878

282/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9879

285/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9880

288/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9880

291/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9881

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9882

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9882

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9883

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5751 - loss: 0.9884

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9885

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9886

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9886

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9887

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9888

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5750 - loss: 0.9889

324/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 0.9889

327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5750 - loss: 0.9890

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9890

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9891

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9891

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9892

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9893

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9893

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9894

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9894

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9895

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5749 - loss: 0.9896

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9897

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9898

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5748 - loss: 0.9898

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9899

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9899

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9900

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9901

390/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9901

393/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9902

396/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9902

399/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9903

402/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9903

405/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9904

408/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9904

411/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9905

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5748 - loss: 0.9905

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5747 - loss: 0.9905

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5747 - loss: 0.9906

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9906

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9907

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9907

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9908

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9909

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9910

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9911

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9911

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9912

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9912

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9913

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9913

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5747 - loss: 0.9914
Epoch 31: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5746 - loss: 0.9916 - val_accuracy: 0.5885 - val_loss: 0.9700 - learning_rate: 4.0000e-04
Epoch 32/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:17 164ms/step - accuracy: 0.5625 - loss: 0.9749

  4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5833 - loss: 0.9748  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5794 - loss: 0.9779

 10/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5792 - loss: 0.9819

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5802 - loss: 0.9873 

 16/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5785 - loss: 0.9920

 18/473 ━━━━━━━━━━━━━━━━━━━━ 10s 23ms/step - accuracy: 0.5768 - loss: 0.9949

 21/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5750 - loss: 0.9973

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5734 - loss: 0.9995 

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5725 - loss: 1.0002

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5714 - loss: 1.0014

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5704 - loss: 1.0030

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5696 - loss: 1.0043

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5694 - loss: 1.0053

 41/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5693 - loss: 1.0056

 44/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5691 - loss: 1.0058

 47/473 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.5690 - loss: 1.0061

 50/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5688 - loss: 1.0064

 53/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5686 - loss: 1.0069

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5685 - loss: 1.0071

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5684 - loss: 1.0075

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5683 - loss: 1.0080

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5682 - loss: 1.0083

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5680 - loss: 1.0085

 71/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5679 - loss: 1.0089

 74/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5678 - loss: 1.0091

 77/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5677 - loss: 1.0094

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5675 - loss: 1.0098

 83/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5674 - loss: 1.0101

 86/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5675 - loss: 1.0102

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5675 - loss: 1.0102

 91/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5674 - loss: 1.0104

 94/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5674 - loss: 1.0105

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5674 - loss: 1.0107

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5674 - loss: 1.0108

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5674 - loss: 1.0108

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5674 - loss: 1.0108

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5675 - loss: 1.0108

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5675 - loss: 1.0108

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469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5697 - loss: 1.0054
Epoch 32: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5697 - loss: 1.0054 - val_accuracy: 0.5861 - val_loss: 0.9732 - learning_rate: 4.0000e-04
Epoch 33/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:22 175ms/step - accuracy: 0.5625 - loss: 0.9450

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5586 - loss: 0.9447   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5623 - loss: 0.9680

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5609 - loss: 0.9812

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5593 - loss: 0.9928

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5573 - loss: 1.0004

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5554 - loss: 1.0053

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5547 - loss: 1.0067

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5556 - loss: 1.0054

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5559 - loss: 1.0050

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 59/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5612 - loss: 1.0002

 62/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5612 - loss: 1.0005

 65/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5613 - loss: 1.0007

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 74/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5609 - loss: 1.0019

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 83/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0032

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 95/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0036

 98/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0037

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0039

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0042

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0044

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 19ms/step - accuracy: 0.5607 - loss: 1.0047

113/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5608 - loss: 1.0049

116/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5608 - loss: 1.0051

119/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5609 - loss: 1.0053

122/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5609 - loss: 1.0055

125/473 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5610 - loss: 1.0057

128/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0058

131/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0060

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0061

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0062

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0063

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0064

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0065

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0065

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0066

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0068

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0069

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0071

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0072

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0073

170/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5615 - loss: 1.0075

173/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5616 - loss: 1.0076

176/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5616 - loss: 1.0077

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5617 - loss: 1.0078

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5618 - loss: 1.0079

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0080

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0081

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0082

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0083

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0084

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0085

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0086

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0087

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5623 - loss: 1.0088

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5624 - loss: 1.0089

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5625 - loss: 1.0090

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5625 - loss: 1.0090

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5626 - loss: 1.0091

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5626 - loss: 1.0092

223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0092

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229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5627 - loss: 1.0094

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5628 - loss: 1.0095

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247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0100

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253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5630 - loss: 1.0102

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0103

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5631 - loss: 1.0103

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274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5633 - loss: 1.0106

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356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110

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361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5638 - loss: 1.0110

366/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5639 - loss: 1.0110

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5639 - loss: 1.0110

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5639 - loss: 1.0110

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5639 - loss: 1.0110

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0110

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0111

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5640 - loss: 1.0111

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5640 - loss: 1.0111

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0111

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5641 - loss: 1.0110
Epoch 33: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5641 - loss: 1.0110 - val_accuracy: 0.5901 - val_loss: 0.9703 - learning_rate: 4.0000e-04
Epoch 34/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:30 191ms/step - accuracy: 0.6875 - loss: 0.9269

  4/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.6895 - loss: 0.8722  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6525 - loss: 0.9065 

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6380 - loss: 0.9201

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6330 - loss: 0.9261

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6271 - loss: 0.9353

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6226 - loss: 0.9407

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6184 - loss: 0.9448

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6137 - loss: 0.9498

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6098 - loss: 0.9539

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6063 - loss: 0.9574

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6030 - loss: 0.9604

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.6002 - loss: 0.9629

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5981 - loss: 0.9647

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5968 - loss: 0.9660

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5948 - loss: 0.9680

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5931 - loss: 0.9700

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5917 - loss: 0.9716

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5903 - loss: 0.9731

 56/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5896 - loss: 0.9740

 59/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5886 - loss: 0.9750

 62/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5877 - loss: 0.9759

 65/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5870 - loss: 0.9764

 68/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5863 - loss: 0.9770

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5858 - loss: 0.9774

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5851 - loss: 0.9780

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5846 - loss: 0.9785

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5840 - loss: 0.9792

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5835 - loss: 0.9798

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5830 - loss: 0.9803

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5825 - loss: 0.9809

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5821 - loss: 0.9814

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5816 - loss: 0.9819

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5812 - loss: 0.9823

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5808 - loss: 0.9828

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5803 - loss: 0.9833

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5799 - loss: 0.9837

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5795 - loss: 0.9841

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5792 - loss: 0.9844

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5787 - loss: 0.9849

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5783 - loss: 0.9853

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5779 - loss: 0.9857

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5776 - loss: 0.9860

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5772 - loss: 0.9865

129/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5768 - loss: 0.9871

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5764 - loss: 0.9877

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5760 - loss: 0.9882

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5757 - loss: 0.9888

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5753 - loss: 0.9894

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5749 - loss: 0.9899

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5746 - loss: 0.9905

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5743 - loss: 0.9910

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5740 - loss: 0.9915

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5737 - loss: 0.9920

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5735 - loss: 0.9924

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5733 - loss: 0.9927

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5731 - loss: 0.9931

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5730 - loss: 0.9934

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5728 - loss: 0.9936

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5727 - loss: 0.9939

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5726 - loss: 0.9941

179/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 0.9944

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 0.9946

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 0.9949

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 0.9951

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5721 - loss: 0.9954

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5720 - loss: 0.9956

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5719 - loss: 0.9959

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5718 - loss: 0.9961

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5718 - loss: 0.9963

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9964

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9966

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5717 - loss: 0.9967

215/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5716 - loss: 0.9969

218/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5716 - loss: 0.9970

221/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5715 - loss: 0.9972

224/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5715 - loss: 0.9974

227/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5714 - loss: 0.9977

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5713 - loss: 0.9978

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5713 - loss: 0.9980

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5712 - loss: 0.9982

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5711 - loss: 0.9985

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5710 - loss: 0.9987

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5709 - loss: 0.9989

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5708 - loss: 0.9991

249/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5707 - loss: 0.9993

252/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5706 - loss: 0.9995

255/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5706 - loss: 0.9997

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5705 - loss: 0.9999

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5704 - loss: 1.0001

264/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5703 - loss: 1.0003

267/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0006

270/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5702 - loss: 1.0007

273/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5701 - loss: 1.0009

276/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5700 - loss: 1.0011

279/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5699 - loss: 1.0013

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5699 - loss: 1.0014

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5698 - loss: 1.0016

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5697 - loss: 1.0017

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5696 - loss: 1.0019

294/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5695 - loss: 1.0021

297/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5694 - loss: 1.0023

300/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5694 - loss: 1.0025

303/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5693 - loss: 1.0026

306/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0028

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5692 - loss: 1.0029

312/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5691 - loss: 1.0031

315/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5690 - loss: 1.0032

318/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0033

321/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5689 - loss: 1.0035

324/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5688 - loss: 1.0036

327/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5687 - loss: 1.0038

330/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5686 - loss: 1.0039

333/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5686 - loss: 1.0040

336/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5685 - loss: 1.0042

339/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0043

342/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5684 - loss: 1.0044

345/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5683 - loss: 1.0046

348/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5682 - loss: 1.0047

351/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5682 - loss: 1.0048

354/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5681 - loss: 1.0049

357/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5680 - loss: 1.0051

360/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5680 - loss: 1.0052

363/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0053

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0054

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5679 - loss: 1.0055

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5678 - loss: 1.0056

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5678 - loss: 1.0057

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0058

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0059

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5677 - loss: 1.0060

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0060

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0061

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5676 - loss: 1.0062

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0063

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0064

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5675 - loss: 1.0065

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0066

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0066

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5674 - loss: 1.0067

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0068

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0069

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5673 - loss: 1.0070

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5672 - loss: 1.0070

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5672 - loss: 1.0071

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0072

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0073

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0073

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5671 - loss: 1.0074

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0075

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0075

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5670 - loss: 1.0076

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0077

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0077

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5669 - loss: 1.0078

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0079

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0079

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5668 - loss: 1.0080

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5667 - loss: 1.0081
Epoch 34: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5667 - loss: 1.0082 - val_accuracy: 0.5897 - val_loss: 0.9754 - learning_rate: 4.0000e-04
Epoch 35/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.6875 - loss: 0.8332

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6133 - loss: 0.9275   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6044 - loss: 0.9532

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6025 - loss: 0.9573

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6018 - loss: 0.9592

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6010 - loss: 0.9611

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6003 - loss: 0.9630

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6001 - loss: 0.9647

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.6000 - loss: 0.9656

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5983 - loss: 0.9682

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5969 - loss: 0.9704

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5960 - loss: 0.9723

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5953 - loss: 0.9739

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5950 - loss: 0.9751

 42/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5949 - loss: 0.9762

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5946 - loss: 0.9777

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5940 - loss: 0.9793

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5932 - loss: 0.9808

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5924 - loss: 0.9819

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5918 - loss: 0.9828

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5912 - loss: 0.9836

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5905 - loss: 0.9845

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5898 - loss: 0.9855

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5892 - loss: 0.9863

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5888 - loss: 0.9869

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5884 - loss: 0.9874

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5881 - loss: 0.9878

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5879 - loss: 0.9882

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5877 - loss: 0.9885

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5875 - loss: 0.9888

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5873 - loss: 0.9891

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5871 - loss: 0.9894

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5870 - loss: 0.9896

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5868 - loss: 0.9899

101/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5866 - loss: 0.9902

104/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5865 - loss: 0.9906

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5863 - loss: 0.9910

110/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5861 - loss: 0.9913

113/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5859 - loss: 0.9916

116/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5858 - loss: 0.9919

119/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5857 - loss: 0.9921

122/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5856 - loss: 0.9923

125/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5856 - loss: 0.9926

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5855 - loss: 0.9928

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5854 - loss: 0.9930

134/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5853 - loss: 0.9931

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5852 - loss: 0.9933

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143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5850 - loss: 0.9936

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5849 - loss: 0.9937

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5847 - loss: 0.9938

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5846 - loss: 0.9938

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5844 - loss: 0.9939

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5843 - loss: 0.9940

161/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5841 - loss: 0.9941

164/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5840 - loss: 0.9941

167/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5839 - loss: 0.9942

170/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5838 - loss: 0.9943

173/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5837 - loss: 0.9944

176/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5835 - loss: 0.9944

179/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5834 - loss: 0.9945

182/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5833 - loss: 0.9946

185/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5832 - loss: 0.9947

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5831 - loss: 0.9948

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5830 - loss: 0.9949

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5829 - loss: 0.9949

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5828 - loss: 0.9950

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5827 - loss: 0.9951

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5827 - loss: 0.9951

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5826 - loss: 0.9952

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5825 - loss: 0.9953

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5824 - loss: 0.9954

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5824 - loss: 0.9955

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5823 - loss: 0.9956

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5822 - loss: 0.9957

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5822 - loss: 0.9958

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5821 - loss: 0.9959

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5820 - loss: 0.9959

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5820 - loss: 0.9960

233/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5819 - loss: 0.9962

236/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5818 - loss: 0.9963

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5817 - loss: 0.9964

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5816 - loss: 0.9966

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5815 - loss: 0.9967

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5815 - loss: 0.9968

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5814 - loss: 0.9970

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5813 - loss: 0.9971

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5812 - loss: 0.9972

258/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5812 - loss: 0.9973

261/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5811 - loss: 0.9975

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5811 - loss: 0.9976

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5810 - loss: 0.9978

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5809 - loss: 0.9979

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5808 - loss: 0.9981

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5807 - loss: 0.9982

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5806 - loss: 0.9984

281/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5806 - loss: 0.9985

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5805 - loss: 0.9986

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5804 - loss: 0.9988

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5803 - loss: 0.9989

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5802 - loss: 0.9991

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5802 - loss: 0.9992

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5801 - loss: 0.9994

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5800 - loss: 0.9995

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5800 - loss: 0.9996

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5799 - loss: 0.9997

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5799 - loss: 0.9998

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5798 - loss: 1.0000

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5798 - loss: 1.0001

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5797 - loss: 1.0001

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5797 - loss: 1.0003

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5797 - loss: 1.0004

329/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5796 - loss: 1.0005

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5796 - loss: 1.0005

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5795 - loss: 1.0006

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5795 - loss: 1.0007

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5795 - loss: 1.0008

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5794 - loss: 1.0009

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5794 - loss: 1.0010

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5793 - loss: 1.0011

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5793 - loss: 1.0012

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5793 - loss: 1.0013

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5792 - loss: 1.0014

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5792 - loss: 1.0014

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5792 - loss: 1.0015

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5791 - loss: 1.0016

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5791 - loss: 1.0017

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5791 - loss: 1.0018

376/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5790 - loss: 1.0019

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5790 - loss: 1.0020

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 1.0020

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 1.0021

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5789 - loss: 1.0022

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 1.0023

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5788 - loss: 1.0024

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5787 - loss: 1.0025

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5787 - loss: 1.0026

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5787 - loss: 1.0027

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5786 - loss: 1.0027

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5786 - loss: 1.0028

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5786 - loss: 1.0029

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0030

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0030

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0031

424/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5785 - loss: 1.0032

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 1.0032

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 1.0033

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 1.0033

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 1.0033

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5784 - loss: 1.0034

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0035

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0035

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5783 - loss: 1.0036

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0037

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0038

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5782 - loss: 1.0038
Epoch 35: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5781 - loss: 1.0039 - val_accuracy: 0.5885 - val_loss: 0.9745 - learning_rate: 4.0000e-04
Epoch 36/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:20 171ms/step - accuracy: 0.7500 - loss: 0.8510

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.6536 - loss: 0.9619   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6405 - loss: 0.9682

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6257 - loss: 0.9732

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6197 - loss: 0.9722

 15/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6168 - loss: 0.9717

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6136 - loss: 0.9713

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.6108 - loss: 0.9710

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386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5734 - loss: 1.0006

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5734 - loss: 1.0007

392/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5733 - loss: 1.0007

395/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5733 - loss: 1.0008

398/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5732 - loss: 1.0009

401/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5732 - loss: 1.0009

404/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5731 - loss: 1.0010

407/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5731 - loss: 1.0010

410/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5730 - loss: 1.0011

413/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5730 - loss: 1.0011

416/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5729 - loss: 1.0012

419/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5729 - loss: 1.0012

422/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5728 - loss: 1.0012

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5728 - loss: 1.0013

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5727 - loss: 1.0013

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0014

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0014

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0015

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0015

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0015

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0016

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0016

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0017

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0017

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0017

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5723 - loss: 1.0018

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0018

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5722 - loss: 1.0018
Epoch 36: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.
Epoch 36: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5721 - loss: 1.0019 - val_accuracy: 0.5913 - val_loss: 0.9738 - learning_rate: 4.0000e-04
Epoch 37/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 162ms/step - accuracy: 0.5938 - loss: 0.9484

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5540 - loss: 1.0645   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.5577 - loss: 1.0545

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5652 - loss: 1.0372

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5677 - loss: 1.0355

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5685 - loss: 1.0344

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5674 - loss: 1.0341

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5667 - loss: 1.0327

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5666 - loss: 1.0318

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5666 - loss: 1.0309

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5670 - loss: 1.0294

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5675 - loss: 1.0280

 37/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5677 - loss: 1.0270

 40/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0263

 43/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0259

 46/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0257

 49/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5679 - loss: 1.0251

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0244

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0242

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0241

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0240

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5678 - loss: 1.0238

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0236

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5679 - loss: 1.0234

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5680 - loss: 1.0230

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5681 - loss: 1.0227

 78/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5682 - loss: 1.0223

 81/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5684 - loss: 1.0218

 84/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5686 - loss: 1.0212

 87/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5689 - loss: 1.0207

 90/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5692 - loss: 1.0201

 93/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5695 - loss: 1.0195

 96/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5697 - loss: 1.0189

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5700 - loss: 1.0184

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5702 - loss: 1.0178

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5704 - loss: 1.0173

107/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5705 - loss: 1.0169

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5706 - loss: 1.0166

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5707 - loss: 1.0161

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5709 - loss: 1.0156

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5711 - loss: 1.0152

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5712 - loss: 1.0148

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5713 - loss: 1.0145

127/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5714 - loss: 1.0141

130/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5715 - loss: 1.0139

133/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5715 - loss: 1.0136

136/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5716 - loss: 1.0133

139/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5717 - loss: 1.0131

142/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5717 - loss: 1.0128

145/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5718 - loss: 1.0125

148/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5718 - loss: 1.0123

151/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5719 - loss: 1.0120

154/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5719 - loss: 1.0117

157/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5720 - loss: 1.0115

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5721 - loss: 1.0112

163/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5721 - loss: 1.0110

166/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0108

169/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0107

172/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5722 - loss: 1.0105

175/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 1.0103

178/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5722 - loss: 1.0101

181/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 1.0099

184/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5723 - loss: 1.0097

187/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 1.0095

190/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5724 - loss: 1.0093

193/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0091

196/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0090

199/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5725 - loss: 1.0088

202/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5726 - loss: 1.0086

205/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5726 - loss: 1.0085

208/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0083

211/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5727 - loss: 1.0081

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5728 - loss: 1.0080

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5728 - loss: 1.0078

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5729 - loss: 1.0076

223/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5729 - loss: 1.0075

226/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0074

229/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0072

232/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5730 - loss: 1.0071

235/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0070

238/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0069

241/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0069

244/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5731 - loss: 1.0068

247/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0067

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0066

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0065

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0064

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268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063

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272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5732 - loss: 1.0063

274/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0062

280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0062

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5732 - loss: 1.0063

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

307/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

309/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5731 - loss: 1.0063

325/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063

328/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063

331/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063

334/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0063

337/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062

340/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062

343/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5731 - loss: 1.0062

346/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0062

349/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0062

352/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061

355/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061

358/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061

361/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0061

364/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060

367/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060

370/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0060

373/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5730 - loss: 1.0059

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0059

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0058

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5730 - loss: 1.0058

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058

414/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0058

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5729 - loss: 1.0059

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5728 - loss: 1.0059

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5728 - loss: 1.0059

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5728 - loss: 1.0059

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0059

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0060

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5727 - loss: 1.0060

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0060

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5726 - loss: 1.0061

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5725 - loss: 1.0061

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5724 - loss: 1.0061
Epoch 37: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.5724 - loss: 1.0062 - val_accuracy: 0.5913 - val_loss: 0.9698 - learning_rate: 8.0000e-05
Epoch 38/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:13 157ms/step - accuracy: 0.5000 - loss: 1.0527

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.4915 - loss: 1.0061   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5249 - loss: 0.9704

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5351 - loss: 0.9633

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5427 - loss: 0.9561

 16/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5487 - loss: 0.9557

 18/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5527 - loss: 0.9548

 21/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5579 - loss: 0.9540

 24/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5614 - loss: 0.9543

 27/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5640 - loss: 0.9551

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5654 - loss: 0.9566

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5657 - loss: 0.9591

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5662 - loss: 0.9617

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5669 - loss: 0.9641

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5679 - loss: 0.9655

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5689 - loss: 0.9667

 48/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5698 - loss: 0.9677

 51/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5704 - loss: 0.9688

 54/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5709 - loss: 0.9697

 57/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5714 - loss: 0.9705

 60/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5719 - loss: 0.9713

 63/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5725 - loss: 0.9717

 66/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5731 - loss: 0.9723

 69/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5737 - loss: 0.9729

 72/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5741 - loss: 0.9737

 75/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5743 - loss: 0.9745

 78/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5746 - loss: 0.9751

 80/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5748 - loss: 0.9755

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5750 - loss: 0.9759

 85/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5753 - loss: 0.9764

 88/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5756 - loss: 0.9769

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5758 - loss: 0.9772

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5760 - loss: 0.9776

 96/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5762 - loss: 0.9778

 99/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5764 - loss: 0.9782

102/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5765 - loss: 0.9785

105/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5767 - loss: 0.9788

108/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5769 - loss: 0.9790

111/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5771 - loss: 0.9792

114/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5773 - loss: 0.9795

117/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5774 - loss: 0.9798

120/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5775 - loss: 0.9800

123/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5777 - loss: 0.9803

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5778 - loss: 0.9805

128/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5779 - loss: 0.9806

131/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5780 - loss: 0.9808

134/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5781 - loss: 0.9810

137/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5782 - loss: 0.9812

140/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5783 - loss: 0.9814

143/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5783 - loss: 0.9817

146/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5784 - loss: 0.9819

149/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5784 - loss: 0.9821

152/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5785 - loss: 0.9823

155/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5785 - loss: 0.9825

158/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9827

160/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9828

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9829

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9830

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5786 - loss: 0.9832

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9833

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9835

177/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9836

180/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5787 - loss: 0.9838

183/473 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.5788 - loss: 0.9839

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9840

188/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9841

191/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9843

194/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9844

197/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9846

200/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9848

203/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9850

206/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9852

209/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9854

212/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9856

214/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9857

217/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5788 - loss: 0.9859

220/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9861

223/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9862

225/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9863

227/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9865

230/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9866

233/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5787 - loss: 0.9868

236/473 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5786 - loss: 0.9870

239/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9872

242/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5786 - loss: 0.9873

245/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9875

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9877

251/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5785 - loss: 0.9878

254/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9880

257/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5784 - loss: 0.9881

260/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 0.9883

263/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5783 - loss: 0.9885

266/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9886

269/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5782 - loss: 0.9888

272/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9890

275/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5781 - loss: 0.9891

278/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5780 - loss: 0.9893

281/473 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - accuracy: 0.5780 - loss: 0.9894

284/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9896

287/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5779 - loss: 0.9897

290/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 0.9898

293/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5778 - loss: 0.9900

296/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9901

299/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9902

302/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5777 - loss: 0.9903

305/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9905

308/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5776 - loss: 0.9906

311/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9907

314/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5775 - loss: 0.9908

317/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9909

320/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5774 - loss: 0.9911

323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 0.9912

326/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5773 - loss: 0.9913

329/473 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.5772 - loss: 0.9914

332/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5772 - loss: 0.9916

335/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9917

338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9918

341/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5771 - loss: 0.9919

344/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9920

347/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5770 - loss: 0.9922

350/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9923

353/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5769 - loss: 0.9924

356/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9925

359/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9926

362/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5768 - loss: 0.9927

365/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9929

368/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9930

371/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5767 - loss: 0.9931

374/473 ━━━━━━━━━━━━━━━━━━━━ 2s 21ms/step - accuracy: 0.5766 - loss: 0.9932

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 0.9933

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5766 - loss: 0.9934

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9935

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9936

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5765 - loss: 0.9937

391/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9938

394/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9939

397/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5764 - loss: 0.9940

400/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9941

403/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9942

406/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5763 - loss: 0.9943

409/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9944

412/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9944

415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9945

417/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5762 - loss: 0.9946

420/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9947

423/473 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5761 - loss: 0.9948

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 0.9948

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5761 - loss: 0.9949

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9950

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9951

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9951

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5760 - loss: 0.9952

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9953

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9954

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5759 - loss: 0.9955

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9955

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9956

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5758 - loss: 0.9957

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9958

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5757 - loss: 0.9959

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.5756 - loss: 0.9960
Epoch 38: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.5756 - loss: 0.9961 - val_accuracy: 0.5913 - val_loss: 0.9712 - learning_rate: 8.0000e-05
Epoch 39/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:16 163ms/step - accuracy: 0.5938 - loss: 1.0910

  4/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5605 - loss: 1.1036   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5415 - loss: 1.1154

 10/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5448 - loss: 1.1067

 13/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5479 - loss: 1.0973

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5493 - loss: 1.0903

 19/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5512 - loss: 1.0844

 22/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5514 - loss: 1.0789

 25/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5509 - loss: 1.0754

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5500 - loss: 1.0734

 30/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5495 - loss: 1.0723

 33/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5490 - loss: 1.0703

 36/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5492 - loss: 1.0681

 39/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5497 - loss: 1.0656

 42/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5502 - loss: 1.0632

 45/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5506 - loss: 1.0612

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415/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0103

418/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0102

421/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5648 - loss: 1.0102

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0101

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0101

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0100

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0099

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0099

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0098

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0097

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5649 - loss: 1.0097

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0097

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0096

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0096

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0095

473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5650 - loss: 1.0094
Epoch 39: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5650 - loss: 1.0094 - val_accuracy: 0.5897 - val_loss: 0.9716 - learning_rate: 8.0000e-05
Epoch 40/40
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:15 159ms/step - accuracy: 0.5000 - loss: 1.1003

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5365 - loss: 1.0657   

  7/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5480 - loss: 1.0649

 10/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5537 - loss: 1.0540

 13/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5573 - loss: 1.0479

 16/473 ━━━━━━━━━━━━━━━━━━━━ 8s 19ms/step - accuracy: 0.5622 - loss: 1.0424

 19/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5646 - loss: 1.0375

 22/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5655 - loss: 1.0338

 25/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.5654 - loss: 1.0318

 28/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5645 - loss: 1.0315

 31/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5639 - loss: 1.0308

 34/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5627 - loss: 1.0306

 36/473 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.5621 - loss: 1.0303

 38/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5616 - loss: 1.0300

 40/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5611 - loss: 1.0299

 43/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5603 - loss: 1.0299

 46/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5594 - loss: 1.0302

 49/473 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.5585 - loss: 1.0305

 52/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5576 - loss: 1.0310

 55/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5570 - loss: 1.0313

 58/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5567 - loss: 1.0313

 61/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5565 - loss: 1.0312

 64/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5564 - loss: 1.0313

 67/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5563 - loss: 1.0312

 70/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5563 - loss: 1.0310

 73/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5564 - loss: 1.0306

 76/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5568 - loss: 1.0299

 79/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5572 - loss: 1.0293

 82/473 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.5576 - loss: 1.0287

 85/473 ━━━━━━━━━━━━━━━━━━━━ 7s 21ms/step - accuracy: 0.5579 - loss: 1.0283

 88/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5583 - loss: 1.0278

 91/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5586 - loss: 1.0274

 94/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5589 - loss: 1.0270

 97/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5591 - loss: 1.0268

100/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5593 - loss: 1.0265

103/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5595 - loss: 1.0262

106/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5597 - loss: 1.0260

109/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5598 - loss: 1.0259

112/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5600 - loss: 1.0257

115/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5602 - loss: 1.0255

118/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5603 - loss: 1.0252

121/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5605 - loss: 1.0250

124/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5607 - loss: 1.0247

126/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5608 - loss: 1.0246

129/473 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5609 - loss: 1.0244

132/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5610 - loss: 1.0242

135/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5611 - loss: 1.0240

138/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5612 - loss: 1.0239

141/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5613 - loss: 1.0237

144/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5614 - loss: 1.0234

147/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5615 - loss: 1.0232

150/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5616 - loss: 1.0231

153/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5617 - loss: 1.0229

156/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5617 - loss: 1.0228

159/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0227

162/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0226

165/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0226

168/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225

171/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225

174/473 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - accuracy: 0.5618 - loss: 1.0225

177/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0225

180/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0224

183/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0224

186/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5619 - loss: 1.0223

189/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0222

192/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0221

195/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5620 - loss: 1.0220

198/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0219

201/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0218

204/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0217

207/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5621 - loss: 1.0216

210/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0215

213/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0214

216/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0214

219/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0213

222/473 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.5622 - loss: 1.0213

225/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0212

228/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0212

231/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5622 - loss: 1.0211

234/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0211

237/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0210

240/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0209

243/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0209

246/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0208

248/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0208

250/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0208

253/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5621 - loss: 1.0207

256/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5620 - loss: 1.0206

259/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5620 - loss: 1.0206

262/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5620 - loss: 1.0205

265/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5620 - loss: 1.0205

268/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5619 - loss: 1.0204

271/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5619 - loss: 1.0204

274/473 ━━━━━━━━━━━━━━━━━━━━ 4s 20ms/step - accuracy: 0.5619 - loss: 1.0203

277/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5618 - loss: 1.0203

280/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5618 - loss: 1.0202

283/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5618 - loss: 1.0202

286/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5618 - loss: 1.0201

289/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5617 - loss: 1.0201

292/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5617 - loss: 1.0200

295/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5617 - loss: 1.0200

298/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5616 - loss: 1.0199

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313/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5614 - loss: 1.0197

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323/473 ━━━━━━━━━━━━━━━━━━━━ 3s 20ms/step - accuracy: 0.5613 - loss: 1.0196

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338/473 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5612 - loss: 1.0195

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377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5613 - loss: 1.0188

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465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5619 - loss: 1.0174
Epoch 40: val_accuracy did not improve from 0.59273

473/473 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.5620 - loss: 1.0173 - val_accuracy: 0.5917 - val_loss: 0.9724 - learning_rate: 8.0000e-05
Restoring model weights from the end of the best epoch: 37.

Plotting the Training and Validation Accuracies¶

In [56]:
plt.plot(history_efficient.history["accuracy"])
plt.plot(history_efficient.history["val_accuracy"])
plt.title("EfficientNet Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the EfficientnetNet Model¶

In [57]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator_efficientnet.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator_efficientnet.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = new_efficient_model.evaluate(test_generator_efficientnet, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.8125 - loss: 0.6692

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.6635 - loss: 0.8448 
Loss: 0.8933918476104736, Accuracy: 0.625

Plotting the confusion matrix¶

In [58]:
pred_probabilities = new_efficient_model.predict(test_generator_efficientnet, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator_efficientnet.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("EfficientNet Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 7s 3s/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 3s 5ms/step
              precision    recall  f1-score   support

       happy       0.53      0.81      0.64        32
     neutral       0.50      0.47      0.48        32
         sad       0.78      0.56      0.65        32
    surprise       0.81      0.66      0.72        32

    accuracy                           0.62       128
   macro avg       0.66      0.62      0.63       128
weighted avg       0.66      0.62      0.63       128

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Observations and Insights:

  • The EfficientNetV2B0 model, with its 5,919,312 total parameters, with no model layers cut, is a balance of complexity and efficiency, suitable for our grayscale 48x48 emotion classification task.
  • On the test set, the model achieved an accuracy of 62.5%.
  • The confusion matrix reveals the model's strengths and weaknesses: it was most precise with 'surprise' (precision of 0.81) and 'sad' (precision of 0.78) emotions, but it struggled to correctly identify 'happy' emotions (with the highest recall at 0.81), suggesting that it often misclassifies other emotions as 'happy'.

Think About It:

  • What is your overall performance of these Transfer Learning Architectures? Can we draw a comparison of these models' performances. Are we satisfied with the accuracies that we have received?
  • Answer: The overall performance of the transfer learning architectures is moderately satisfactory. They are capable of learning from the dataset, but clearly there is still room for improvement. We have tested cutting the models in different layers and playing with different fully connected layers, but the result did not improve significantly. Maybe it requires to go deeper to the nature of each model to understand what changes need to be made to get the most out of them.
  • Do you think our issue lies with 'rgb' color_mode?
  • Answer: It may be one of the possible issues, but not the main one. I would go more on the type of images (and also size), on which these models were trained for. So that, the features learned cannot be used on our task in hand.

Now that we have tried multiple pre-trained models, let's build a complex CNN architecture and see if we can get better performance.

Building a Complex Neural Network Architecture¶

In this section, we will build a more complex Convolutional Neural Network Model that has close to as many parameters as we had in our Transfer Learning Models. However, we will have only 1 input channel for our input images.

Creating our Data Loaders¶

In this section, we are creating data loaders which we will use as inputs to the more Complicated Convolutional Neural Network. We will go ahead with color_mode = 'grayscale'.

In [59]:
# Set this to 'grayscale' as the images are in grayscale
color_mode = "grayscale"
color_layers = 1
# As we have checked, all images are 48x48, we will set the img_width and img_height to 48
img_width, img_height = 48, 48
# A batch size of 32 is appropriate for this dataset provide to provide a good balance
# between the model's ability to generalize (avoid overfitting) and computational efficiency.
batch_size = 32

# Training Data Augmentation
train_datagen = ImageDataGenerator(
    rescale=1.0 / 255,  # Normalize pixel values to [0,1]
    horizontal_flip=True,  # Faces are symmetric; flipping can simulate looking from another direction
    brightness_range=(0.5, 1.5),  # Randomly adjust brightness to simulate different lighting conditions
    shear_range=0.3,  # Shear transformations for perspective changes
    rotation_range=20,  # Slight rotation to introduce variability without distorting emotion features
    width_shift_range=0.1,  # Slight horizontal shifts to simulate off-center faces
    height_shift_range=0.1,  # Slight vertical shifts to account for different heights/angles
    zoom_range=0.1,  # Small zoom in/out to simulate closer or further away faces
)

# Validation and Testing Data should not be augmented!
validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_datagen = ImageDataGenerator(rescale=1.0 / 255)

# Creating train_dir, validation_dir, and test_dir with the structure of DATADIR and SUBDIRS
train_dir = os.path.join(DATADIR, SUBDIRS_DICT["train"])
validation_dir = os.path.join(DATADIR, SUBDIRS_DICT["validation"])
test_dir = os.path.join(DATADIR, SUBDIRS_DICT["test"])

# Train Generator
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
)

# Validation Generator
validation_generator = validation_datagen.flow_from_directory(
    validation_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for evaluation
)

# Testing Generator
test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode=color_mode,  # Set to 'grayscale'
    class_mode="categorical",
    shuffle=False,  # shuffle=False to keep data in order for testing
)
Found 15109 images belonging to 4 classes.
Found 4977 images belonging to 4 classes.
Found 128 images belonging to 4 classes.

Model Building¶

  • Try building a layer with 5 Convolutional Blocks and see if performance increases.
In [60]:
backend.clear_session()

# Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(42)

random.seed(42)
tf.random.set_seed(42)
In [61]:
# Initializing a sequential model
model_complex = Sequential()

model_complex.add(Input(shape=(img_width, img_height, color_layers)))

# First Convolutional Block
model_complex.add(Conv2D(64, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))

# Second Convolutional Block
model_complex.add(Conv2D(128, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))

# Third Convolutional Block
model_complex.add(Conv2D(256, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))

# Fourth Convolutional Block
model_complex.add(Conv2D(512, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))

# Fifth Convolutional Block
model_complex.add(Conv2D(128, kernel_size=2, padding="same"))
model_complex.add(BatchNormalization())
model_complex.add(LeakyReLU(negative_slope=0.1))
model_complex.add(MaxPooling2D(pool_size=2))

# Flatten the output of the conv layers to feed into the dense layers
model_complex.add(Flatten())

model_complex.add(Dense(512, activation="relu"))
model_complex.add(Dense(128, activation="relu"))
model_complex.add(Dense(64))
model_complex.add(BatchNormalization())
model_complex.add(ReLU())  # Using ReLU after batch normalization
model_complex.add(Dense(4, activation="softmax"))

# Using RMSProp Optimizer
optimizer = RMSprop(learning_rate=0.01)

Compiling and Training the Model¶

In [62]:
model_complex.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
model_complex.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 48, 48, 64)     │           320 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 48, 48, 64)     │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu (LeakyReLU)         │ (None, 48, 48, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 24, 24, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 24, 24, 128)    │        32,896 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 24, 24, 128)    │           512 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_1 (LeakyReLU)       │ (None, 24, 24, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 12, 12, 256)    │       131,328 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_2           │ (None, 12, 12, 256)    │         1,024 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_2 (LeakyReLU)       │ (None, 12, 12, 256)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_2 (MaxPooling2D)  │ (None, 6, 6, 256)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_3 (Conv2D)               │ (None, 6, 6, 512)      │       524,800 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_3           │ (None, 6, 6, 512)      │         2,048 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_3 (LeakyReLU)       │ (None, 6, 6, 512)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_3 (MaxPooling2D)  │ (None, 3, 3, 512)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_4 (Conv2D)               │ (None, 3, 3, 128)      │       262,272 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_4           │ (None, 3, 3, 128)      │           512 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_4 (LeakyReLU)       │ (None, 3, 3, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_4 (MaxPooling2D)  │ (None, 1, 1, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 512)            │        66,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 128)            │        65,664 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 64)             │         8,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_5           │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu (ReLU)                    │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 1,096,452 (4.18 MB)
 Trainable params: 1,094,148 (4.17 MB)
 Non-trainable params: 2,304 (9.00 KB)
In [63]:
# Get the current time
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")

# Set up Early Stopping with a patience 7 but acting after at least 30 epochs
delayed_early_stopping = DelayedEarlyStopping(
    monitor="val_loss", patience=7, verbose=1, restore_best_weights=True, start_epoch=30
)

# Define the learning rate scheduler callback
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=0.00001, verbose=1)

# Define the saving the best model callback
mc = ModelCheckpoint(
    f"{results_path}/best_model_complex_{current_time}.keras",
    monitor="val_accuracy",
    mode="max",
    verbose=1,
    save_best_only=True,
)

# Fitting the model with 60 epochs and using validation set
history_complex = model_complex.fit(
    train_generator,
    epochs=60,
    validation_data=validation_generator,
    callbacks=[reduce_lr, mc, delayed_early_stopping],
)
Epoch 1/60
/home/iamtxena/sandbox/mit-ai/my_env/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
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Epoch 1: val_accuracy improved from -inf to 0.44967, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

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Epoch 2/60
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Epoch 2: val_accuracy improved from 0.44967 to 0.54872, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.4387 - loss: 1.1926 - val_accuracy: 0.5487 - val_loss: 1.0995 - learning_rate: 0.0100
Epoch 3/60
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Epoch 3: val_accuracy improved from 0.54872 to 0.57304, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.5218 - loss: 1.0473 - val_accuracy: 0.5730 - val_loss: 0.9766 - learning_rate: 0.0100
Epoch 4/60
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Epoch 4: val_accuracy improved from 0.57304 to 0.58891, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5627 - loss: 0.9599 - val_accuracy: 0.5889 - val_loss: 0.9360 - learning_rate: 0.0100
Epoch 5/60
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Epoch 5: val_accuracy did not improve from 0.58891

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.5759 - loss: 0.9249 - val_accuracy: 0.3789 - val_loss: 1.5197 - learning_rate: 0.0100
Epoch 6/60
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Epoch 6: val_accuracy improved from 0.58891 to 0.62829, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6018 - loss: 0.8820 - val_accuracy: 0.6283 - val_loss: 0.9162 - learning_rate: 0.0100
Epoch 7/60
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Epoch 7: val_accuracy improved from 0.62829 to 0.69761, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6136 - loss: 0.8723 - val_accuracy: 0.6976 - val_loss: 0.7406 - learning_rate: 0.0100
Epoch 8/60
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110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6672 - loss: 0.8104

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6666 - loss: 0.8109

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6660 - loss: 0.8115

125/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6655 - loss: 0.8120

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6650 - loss: 0.8124

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6646 - loss: 0.8127

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6642 - loss: 0.8131

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6639 - loss: 0.8135

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6635 - loss: 0.8139

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6631 - loss: 0.8144

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6626 - loss: 0.8149

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6622 - loss: 0.8153

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6618 - loss: 0.8157

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6614 - loss: 0.8161

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6610 - loss: 0.8165

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6607 - loss: 0.8168

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6603 - loss: 0.8172

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6600 - loss: 0.8175

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6597 - loss: 0.8178

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6594 - loss: 0.8181

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6592 - loss: 0.8183

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6588 - loss: 0.8187

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6585 - loss: 0.8191

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6581 - loss: 0.8194

221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6578 - loss: 0.8197

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6576 - loss: 0.8201

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6573 - loss: 0.8204

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6571 - loss: 0.8206

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6568 - loss: 0.8209

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6566 - loss: 0.8213

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6563 - loss: 0.8216

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6561 - loss: 0.8218

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6559 - loss: 0.8221

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6557 - loss: 0.8223

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6556 - loss: 0.8226

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6554 - loss: 0.8228

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6552 - loss: 0.8230

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6551 - loss: 0.8232

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6550 - loss: 0.8235

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6548 - loss: 0.8237

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6547 - loss: 0.8239

305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6546 - loss: 0.8241

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6544 - loss: 0.8243

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6543 - loss: 0.8244

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6542 - loss: 0.8246

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6541 - loss: 0.8247

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6541 - loss: 0.8249

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6540 - loss: 0.8250

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8251

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8251

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6539 - loss: 0.8252

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6538 - loss: 0.8253

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6538 - loss: 0.8253

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8254

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8255

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6537 - loss: 0.8255

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6536 - loss: 0.8256

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6536 - loss: 0.8257

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6536 - loss: 0.8258

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8258

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8259

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8260

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6535 - loss: 0.8260

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8261

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8262

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6534 - loss: 0.8263
Epoch 8: val_accuracy did not improve from 0.69761

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6534 - loss: 0.8263 - val_accuracy: 0.5724 - val_loss: 1.0251 - learning_rate: 0.0100
Epoch 9/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.7188 - loss: 0.6935

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6993 - loss: 0.7490  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6895 - loss: 0.7740

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6858 - loss: 0.7805

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6834 - loss: 0.7863

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6819 - loss: 0.7912

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6802 - loss: 0.7974

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6787 - loss: 0.8011

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6782 - loss: 0.8020

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6775 - loss: 0.8021

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6768 - loss: 0.8021

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6761 - loss: 0.8020

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6756 - loss: 0.8022

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6751 - loss: 0.8026

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6745 - loss: 0.8031

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6742 - loss: 0.8034

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6738 - loss: 0.8038

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6734 - loss: 0.8041

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6732 - loss: 0.8042

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6730 - loss: 0.8040

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6729 - loss: 0.8038

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6728 - loss: 0.8036

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6727 - loss: 0.8035

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6725 - loss: 0.8033

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6724 - loss: 0.8031

126/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6722 - loss: 0.8029

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8026

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8023

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8019

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8015

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8012

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6721 - loss: 0.8008

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6720 - loss: 0.8006

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6720 - loss: 0.8004

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6719 - loss: 0.8002

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6719 - loss: 0.8001

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6718 - loss: 0.8000

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6717 - loss: 0.8000

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6715 - loss: 0.8000

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6714 - loss: 0.8000

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6713 - loss: 0.8000

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6711 - loss: 0.8001

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6710 - loss: 0.8003

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6708 - loss: 0.8004

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6708 - loss: 0.8005

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6707 - loss: 0.8006

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6706 - loss: 0.8006

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6706 - loss: 0.8006

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6705 - loss: 0.8006

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6704 - loss: 0.8006

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8007

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6703 - loss: 0.8007

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6702 - loss: 0.8008

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6702 - loss: 0.8008

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6701 - loss: 0.8009

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6701 - loss: 0.8009

303/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6700 - loss: 0.8010

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.8011

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6699 - loss: 0.8011

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6698 - loss: 0.8012

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.8012

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6697 - loss: 0.8013

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6696 - loss: 0.8014

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6695 - loss: 0.8014

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.8015

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6694 - loss: 0.8015

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.8015

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6693 - loss: 0.8015

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.8015

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6692 - loss: 0.8015

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6691 - loss: 0.8016

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6690 - loss: 0.8016

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.8017

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6689 - loss: 0.8017

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6688 - loss: 0.8018

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6687 - loss: 0.8019

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6687 - loss: 0.8019

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6686 - loss: 0.8019

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6685 - loss: 0.8020

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6685 - loss: 0.8020

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6684 - loss: 0.8021

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6683 - loss: 0.8022

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6683 - loss: 0.8022

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6682 - loss: 0.8023

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6682 - loss: 0.8023

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6681 - loss: 0.8024

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6681 - loss: 0.8024

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6680 - loss: 0.8025

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6680 - loss: 0.8025

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6679 - loss: 0.8026

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6679 - loss: 0.8026

469/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6678 - loss: 0.8027
Epoch 9: val_accuracy did not improve from 0.69761

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6678 - loss: 0.8027 - val_accuracy: 0.6416 - val_loss: 0.8362 - learning_rate: 0.0100
Epoch 10/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.5625 - loss: 0.9427

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.5990 - loss: 0.9089  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6241 - loss: 0.8765

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6355 - loss: 0.8574

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6447 - loss: 0.8410

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6532 - loss: 0.8264

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6581 - loss: 0.8185

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6598 - loss: 0.8161

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6614 - loss: 0.8141

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6626 - loss: 0.8130

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6636 - loss: 0.8118

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6647 - loss: 0.8103

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6654 - loss: 0.8092

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6661 - loss: 0.8082

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6672 - loss: 0.8072

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6679 - loss: 0.8066

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6683 - loss: 0.8062

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6689 - loss: 0.8056

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6694 - loss: 0.8050

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6700 - loss: 0.8044

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6703 - loss: 0.8039

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6706 - loss: 0.8035

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6710 - loss: 0.8030

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6713 - loss: 0.8023

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6717 - loss: 0.8017

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6720 - loss: 0.8011

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6724 - loss: 0.8005

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6729 - loss: 0.7997

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6733 - loss: 0.7990

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6736 - loss: 0.7984

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6739 - loss: 0.7978

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6741 - loss: 0.7973

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6743 - loss: 0.7968

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6745 - loss: 0.7964

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6746 - loss: 0.7959

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6748 - loss: 0.7956

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6749 - loss: 0.7953

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6750 - loss: 0.7950

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6751 - loss: 0.7946

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6752 - loss: 0.7943

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6753 - loss: 0.7940

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6754 - loss: 0.7937

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6755 - loss: 0.7934

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6756 - loss: 0.7930

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6757 - loss: 0.7927

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6757 - loss: 0.7924

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6758 - loss: 0.7921

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6759 - loss: 0.7918

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6759 - loss: 0.7914

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6760 - loss: 0.7911

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6761 - loss: 0.7907

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6762 - loss: 0.7904

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6763 - loss: 0.7900

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6764 - loss: 0.7897

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6765 - loss: 0.7895

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6766 - loss: 0.7893

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6767 - loss: 0.7891

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6767 - loss: 0.7889

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7887

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6768 - loss: 0.7885

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6769 - loss: 0.7883

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6769 - loss: 0.7882

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6770 - loss: 0.7881

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6770 - loss: 0.7879

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6771 - loss: 0.7877

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6771 - loss: 0.7876

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7875

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7874

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7873

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7872

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6772 - loss: 0.7871

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7870

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7869

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7869

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7868

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7867

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7867

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7866

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6773 - loss: 0.7865

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7864

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7864

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7863

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7863

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7862

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7861

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7861

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7860

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7860

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7859

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7858

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7858

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7857

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7857

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7856

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6773 - loss: 0.7855
Epoch 10: val_accuracy did not improve from 0.69761

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6773 - loss: 0.7855 - val_accuracy: 0.6448 - val_loss: 0.8493 - learning_rate: 0.0100
Epoch 11/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.6562 - loss: 0.7888

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6807 - loss: 0.7486  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6873 - loss: 0.7464

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6925 - loss: 0.7370

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6945 - loss: 0.7330

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6959 - loss: 0.7304

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6969 - loss: 0.7295

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6972 - loss: 0.7301

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6973 - loss: 0.7323

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6967 - loss: 0.7352

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6956 - loss: 0.7387

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6945 - loss: 0.7413

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6937 - loss: 0.7429

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6934 - loss: 0.7435

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6930 - loss: 0.7441

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6926 - loss: 0.7447

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6920 - loss: 0.7458

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6914 - loss: 0.7466

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6907 - loss: 0.7476

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6901 - loss: 0.7484

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6897 - loss: 0.7490

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6894 - loss: 0.7496

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6892 - loss: 0.7502

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6889 - loss: 0.7506

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6887 - loss: 0.7511

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6884 - loss: 0.7516

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6882 - loss: 0.7522

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7528

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6877 - loss: 0.7534

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6875 - loss: 0.7540

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6872 - loss: 0.7546

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6870 - loss: 0.7551

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6868 - loss: 0.7557

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6866 - loss: 0.7563

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6865 - loss: 0.7567

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6864 - loss: 0.7572

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6862 - loss: 0.7575

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6861 - loss: 0.7579

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6859 - loss: 0.7582

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6858 - loss: 0.7585

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6857 - loss: 0.7588

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6856 - loss: 0.7592

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6855 - loss: 0.7595

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6854 - loss: 0.7598

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6853 - loss: 0.7601

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6852 - loss: 0.7605

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6851 - loss: 0.7608

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6850 - loss: 0.7611

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6849 - loss: 0.7614

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6849 - loss: 0.7617

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6848 - loss: 0.7619

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6848 - loss: 0.7622

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6847 - loss: 0.7625

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6847 - loss: 0.7627

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6846 - loss: 0.7630

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6846 - loss: 0.7632

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6845 - loss: 0.7634

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6845 - loss: 0.7636

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6844 - loss: 0.7638

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6843 - loss: 0.7641

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6842 - loss: 0.7643

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6842 - loss: 0.7645

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6841 - loss: 0.7646

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6840 - loss: 0.7648

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6839 - loss: 0.7650

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6838 - loss: 0.7652

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6838 - loss: 0.7653

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6837 - loss: 0.7655

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6836 - loss: 0.7656

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6836 - loss: 0.7658

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7659

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7661

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6835 - loss: 0.7662

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7663

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7664

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6834 - loss: 0.7665

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6833 - loss: 0.7666

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6833 - loss: 0.7667

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6832 - loss: 0.7669

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6832 - loss: 0.7670

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6832 - loss: 0.7671

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7672

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7673

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6831 - loss: 0.7674

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6830 - loss: 0.7675

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6830 - loss: 0.7676

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6829 - loss: 0.7677

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6829 - loss: 0.7678

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7679

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7680

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6828 - loss: 0.7681

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7682

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7682

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7683

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7684

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6827 - loss: 0.7684
Epoch 11: val_accuracy did not improve from 0.69761

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6826 - loss: 0.7685 - val_accuracy: 0.6446 - val_loss: 0.8730 - learning_rate: 0.0100
Epoch 12/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.5180

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Epoch 12: val_accuracy improved from 0.69761 to 0.71529, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6850 - loss: 0.7711 - val_accuracy: 0.7153 - val_loss: 0.7055 - learning_rate: 0.0100
Epoch 13/60
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128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6868 - loss: 0.7583

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6869 - loss: 0.7584

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6871 - loss: 0.7583

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6872 - loss: 0.7583

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6874 - loss: 0.7585

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6874 - loss: 0.7585

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6875 - loss: 0.7586

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6877 - loss: 0.7586

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6878 - loss: 0.7586

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6879 - loss: 0.7587

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6879 - loss: 0.7588

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7589

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7590

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7591

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6880 - loss: 0.7592

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6881 - loss: 0.7593

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6881 - loss: 0.7594

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6882 - loss: 0.7594

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6883 - loss: 0.7594

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6884 - loss: 0.7594

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6886 - loss: 0.7593

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6887 - loss: 0.7593

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6887 - loss: 0.7592

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6888 - loss: 0.7592

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6889 - loss: 0.7591

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6890 - loss: 0.7590

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6890 - loss: 0.7589

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6891 - loss: 0.7588

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6892 - loss: 0.7587

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6893 - loss: 0.7586

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6893 - loss: 0.7585

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7584

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7583

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6894 - loss: 0.7581

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6895 - loss: 0.7580

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6895 - loss: 0.7579

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6896 - loss: 0.7578

305/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.6896 - loss: 0.7577

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6897 - loss: 0.7576

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6897 - loss: 0.7575

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7574

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7573

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6898 - loss: 0.7573

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6899 - loss: 0.7572

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6899 - loss: 0.7571

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7570

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7569

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6900 - loss: 0.7569

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7568

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7567

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6901 - loss: 0.7567

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7566

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7566

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6902 - loss: 0.7565

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6903 - loss: 0.7565

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6903 - loss: 0.7564

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6904 - loss: 0.7563

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6904 - loss: 0.7562

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7561

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7561

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6905 - loss: 0.7560

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6906 - loss: 0.7559

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6906 - loss: 0.7558

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6907 - loss: 0.7557

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6907 - loss: 0.7556

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6908 - loss: 0.7555

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6909 - loss: 0.7555

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6909 - loss: 0.7554

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6910 - loss: 0.7553

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6910 - loss: 0.7552

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6911 - loss: 0.7551
Epoch 13: val_accuracy did not improve from 0.71529

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6911 - loss: 0.7551 - val_accuracy: 0.5863 - val_loss: 0.9923 - learning_rate: 0.0100
Epoch 14/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.8438 - loss: 0.6637

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7689 - loss: 0.7116  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7272 - loss: 0.7679

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7093 - loss: 0.7884

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6992 - loss: 0.7956

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6937 - loss: 0.7944

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6901 - loss: 0.7922

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6884 - loss: 0.7891

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6872 - loss: 0.7864

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.6870 - loss: 0.7824

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6874 - loss: 0.7781

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6876 - loss: 0.7750

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6878 - loss: 0.7727

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6882 - loss: 0.7707

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6885 - loss: 0.7689

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6888 - loss: 0.7675

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6891 - loss: 0.7660

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6896 - loss: 0.7645

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6902 - loss: 0.7630

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6905 - loss: 0.7623

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6908 - loss: 0.7616

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6910 - loss: 0.7611

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6911 - loss: 0.7608

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6912 - loss: 0.7604

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6912 - loss: 0.7600

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6913 - loss: 0.7595

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6913 - loss: 0.7592

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6913 - loss: 0.7587

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6914 - loss: 0.7583

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6914 - loss: 0.7578

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6915 - loss: 0.7575

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6915 - loss: 0.7572

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6916 - loss: 0.7570

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6916 - loss: 0.7566

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6917 - loss: 0.7562

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6917 - loss: 0.7560

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6918 - loss: 0.7556

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6919 - loss: 0.7550

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6921 - loss: 0.7544

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6922 - loss: 0.7540

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6924 - loss: 0.7535

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6926 - loss: 0.7530

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6927 - loss: 0.7525

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6929 - loss: 0.7521

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6930 - loss: 0.7517

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6931 - loss: 0.7514

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6932 - loss: 0.7512

221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6932 - loss: 0.7510

226/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6934 - loss: 0.7506

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Epoch 14: val_accuracy improved from 0.71529 to 0.73157, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.6952 - loss: 0.7444 - val_accuracy: 0.7316 - val_loss: 0.6710 - learning_rate: 0.0100
Epoch 15/60
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365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7057 - loss: 0.7135

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375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7056 - loss: 0.7138

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7056 - loss: 0.7140

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7055 - loss: 0.7141

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7055 - loss: 0.7143

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7055 - loss: 0.7144

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7145

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7146

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7147

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7148

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7149

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7150

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7150

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7151

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7152

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7153

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7154

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7054 - loss: 0.7155

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7053 - loss: 0.7156

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7053 - loss: 0.7156
Epoch 15: val_accuracy did not improve from 0.73157

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7053 - loss: 0.7157 - val_accuracy: 0.7135 - val_loss: 0.7173 - learning_rate: 0.0100
Epoch 16/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.5625 - loss: 1.0065

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6408 - loss: 0.8431  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6586 - loss: 0.8039

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6674 - loss: 0.7866

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6720 - loss: 0.7740

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6740 - loss: 0.7678

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6754 - loss: 0.7644

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6768 - loss: 0.7624

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6778 - loss: 0.7606

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6790 - loss: 0.7590

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6804 - loss: 0.7581

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6819 - loss: 0.7574

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6837 - loss: 0.7558

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6856 - loss: 0.7539

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6874 - loss: 0.7519

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6890 - loss: 0.7498

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6903 - loss: 0.7480

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6914 - loss: 0.7467

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6924 - loss: 0.7454

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6933 - loss: 0.7442

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6942 - loss: 0.7429

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6951 - loss: 0.7417

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6958 - loss: 0.7406

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6964 - loss: 0.7396

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6970 - loss: 0.7386

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.6975 - loss: 0.7375

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6979 - loss: 0.7367

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6983 - loss: 0.7357

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6987 - loss: 0.7350

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6990 - loss: 0.7342

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6993 - loss: 0.7335

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.6997 - loss: 0.7329

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7000 - loss: 0.7323

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7002 - loss: 0.7318

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7004 - loss: 0.7314

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7005 - loss: 0.7311

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7007 - loss: 0.7308

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7008 - loss: 0.7306

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7009 - loss: 0.7304

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7010 - loss: 0.7302

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7011 - loss: 0.7300

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7013 - loss: 0.7297

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7014 - loss: 0.7295

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7015 - loss: 0.7292

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7017 - loss: 0.7289

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7018 - loss: 0.7287

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7019 - loss: 0.7284

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7020 - loss: 0.7282

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7021 - loss: 0.7279

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7023 - loss: 0.7276

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7024 - loss: 0.7274

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7025 - loss: 0.7271

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7027 - loss: 0.7268

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7028 - loss: 0.7266

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7029 - loss: 0.7264

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7030 - loss: 0.7262

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7031 - loss: 0.7260

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7032 - loss: 0.7258

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7033 - loss: 0.7257

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7033 - loss: 0.7255

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7034 - loss: 0.7254

302/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7035 - loss: 0.7253

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7036 - loss: 0.7251

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7036 - loss: 0.7250

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7037 - loss: 0.7249

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7038 - loss: 0.7247

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7038 - loss: 0.7246

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7039 - loss: 0.7245

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7244

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7040 - loss: 0.7242

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7241

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7041 - loss: 0.7240

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7042 - loss: 0.7239

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7043 - loss: 0.7238

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7043 - loss: 0.7237

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7044 - loss: 0.7236

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7044 - loss: 0.7235

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7045 - loss: 0.7233

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7045 - loss: 0.7232

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7046 - loss: 0.7232

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7046 - loss: 0.7231

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7047 - loss: 0.7230

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7047 - loss: 0.7229

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7048 - loss: 0.7228

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7048 - loss: 0.7227

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7226

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7226

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7225

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7049 - loss: 0.7224

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7050 - loss: 0.7224
Epoch 16: val_accuracy did not improve from 0.73157

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7050 - loss: 0.7224 - val_accuracy: 0.6954 - val_loss: 0.7775 - learning_rate: 0.0100
Epoch 17/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:12 154ms/step - accuracy: 0.5938 - loss: 0.8235

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6789 - loss: 0.7218   

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6890 - loss: 0.7009

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6953 - loss: 0.6908

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Epoch 17: val_accuracy improved from 0.73157 to 0.73639, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7042 - loss: 0.7132 - val_accuracy: 0.7364 - val_loss: 0.6671 - learning_rate: 0.0100
Epoch 18/60
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147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7032

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7033

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7034

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7034

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7035

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7036

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7037

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7037

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7039

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7094 - loss: 0.7040

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7042

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7093 - loss: 0.7043

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7045

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7046

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7092 - loss: 0.7046

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7047

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7048

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7049

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7049

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7050

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7050

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7051

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7051

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7052

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7052

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7052

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7053

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7052

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7052

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7093 - loss: 0.7051

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7093 - loss: 0.7051

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7094 - loss: 0.7050

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7095 - loss: 0.7049

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7095 - loss: 0.7049

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7048

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7048

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7096 - loss: 0.7047

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7047

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7047

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7097 - loss: 0.7046

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7046

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7045

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7098 - loss: 0.7045

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7045

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7045

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7099 - loss: 0.7044

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7100 - loss: 0.7044

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7044

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7044

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7100 - loss: 0.7043

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7043

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7101 - loss: 0.7042

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7041

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7102 - loss: 0.7040
Epoch 18: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7102 - loss: 0.7040 - val_accuracy: 0.7151 - val_loss: 0.6985 - learning_rate: 0.0100
Epoch 19/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.7188 - loss: 0.6711

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7308 - loss: 0.6339  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7283 - loss: 0.6507

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7304 - loss: 0.6562

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7296 - loss: 0.6586

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7287 - loss: 0.6592

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7270 - loss: 0.6615

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7253 - loss: 0.6647

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7242 - loss: 0.6671

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7234 - loss: 0.6690

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7233 - loss: 0.6700

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7230 - loss: 0.6710

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7222 - loss: 0.6724

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7216 - loss: 0.6737

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7211 - loss: 0.6743

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7207 - loss: 0.6749

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7202 - loss: 0.6758

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7197 - loss: 0.6769

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7193 - loss: 0.6780

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7188 - loss: 0.6792

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7184 - loss: 0.6803

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7181 - loss: 0.6809

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6813

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6819

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7178 - loss: 0.6826

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7176 - loss: 0.6833

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7174 - loss: 0.6839

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7172 - loss: 0.6846

140/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7171 - loss: 0.6851

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7169 - loss: 0.6856

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7169 - loss: 0.6859

155/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7168 - loss: 0.6863

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7167 - loss: 0.6866

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7166 - loss: 0.6869

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7165 - loss: 0.6872

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7164 - loss: 0.6876

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7163 - loss: 0.6880

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7162 - loss: 0.6882

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7161 - loss: 0.6885

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7160 - loss: 0.6888

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7159 - loss: 0.6890

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7159 - loss: 0.6892

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7158 - loss: 0.6894

215/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7158 - loss: 0.6895

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7157 - loss: 0.6896

225/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6898

230/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6899

235/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7156 - loss: 0.6900

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7155 - loss: 0.6900

245/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7155 - loss: 0.6901

250/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7154 - loss: 0.6902

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7154 - loss: 0.6903

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7153 - loss: 0.6904

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7153 - loss: 0.6905

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7152 - loss: 0.6906

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7151 - loss: 0.6907

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7150 - loss: 0.6908

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7150 - loss: 0.6909

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7149 - loss: 0.6910

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7149 - loss: 0.6911

300/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7148 - loss: 0.6912

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7147 - loss: 0.6913

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7146 - loss: 0.6914

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7145 - loss: 0.6916

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7145 - loss: 0.6917

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7144 - loss: 0.6918

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7144 - loss: 0.6919

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7143 - loss: 0.6919

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7143 - loss: 0.6920

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7142 - loss: 0.6921

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7141 - loss: 0.6923

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7141 - loss: 0.6924

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7140 - loss: 0.6925

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7140 - loss: 0.6927

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7139 - loss: 0.6928

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7139 - loss: 0.6929

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7138 - loss: 0.6930

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7138 - loss: 0.6931

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6932

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6933

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6934

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7137 - loss: 0.6935

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7136 - loss: 0.6936

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7136 - loss: 0.6937

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6938

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6939

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7135 - loss: 0.6940

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6941

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6942

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6943

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7134 - loss: 0.6944

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6945

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6946

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7133 - loss: 0.6946
Epoch 19: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7132 - loss: 0.6948 - val_accuracy: 0.6484 - val_loss: 0.8758 - learning_rate: 0.0100
Epoch 20/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.6875 - loss: 0.7259

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6937 - loss: 0.7433  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6996 - loss: 0.7294

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7042 - loss: 0.7285

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7061 - loss: 0.7263

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7077 - loss: 0.7225

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7077 - loss: 0.7215

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7074 - loss: 0.7191

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7071 - loss: 0.7178

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7066 - loss: 0.7166

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.7157

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7064 - loss: 0.7159

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7064 - loss: 0.7152

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7063 - loss: 0.7146

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7065 - loss: 0.7141

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7067 - loss: 0.7136

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7069 - loss: 0.7131

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7128

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7070 - loss: 0.7131

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7134

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7135

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7071 - loss: 0.7136

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7072 - loss: 0.7134

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7074 - loss: 0.7131

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7076 - loss: 0.7127

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7078 - loss: 0.7125

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7079 - loss: 0.7125

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7124

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7123

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7123

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7122

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.7122

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7122

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7123

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7124

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7124

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7079 - loss: 0.7123

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7122

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7080 - loss: 0.7121

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7081 - loss: 0.7120

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7082 - loss: 0.7119

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7083 - loss: 0.7117

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7084 - loss: 0.7116

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7085 - loss: 0.7115

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7086 - loss: 0.7114

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7087 - loss: 0.7112

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7088 - loss: 0.7111

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7089 - loss: 0.7110

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7090 - loss: 0.7109

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7091 - loss: 0.7107

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7092 - loss: 0.7106

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7093 - loss: 0.7105

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7094 - loss: 0.7103

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7095 - loss: 0.7101

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7097 - loss: 0.7100

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7098 - loss: 0.7098

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7099 - loss: 0.7097

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7100 - loss: 0.7095

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7101 - loss: 0.7093

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7102 - loss: 0.7091

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7104 - loss: 0.7089

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7105 - loss: 0.7087

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7106 - loss: 0.7085

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7107 - loss: 0.7083

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7108 - loss: 0.7081

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7109 - loss: 0.7078

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7111 - loss: 0.7076

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7112 - loss: 0.7074

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7113 - loss: 0.7072

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7114 - loss: 0.7069

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7115 - loss: 0.7066

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7116 - loss: 0.7064

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7117 - loss: 0.7062

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.7061

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7118 - loss: 0.7059

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7119 - loss: 0.7057

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.7056

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7120 - loss: 0.7054

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7121 - loss: 0.7053

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7122 - loss: 0.7051

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7122 - loss: 0.7050

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7123 - loss: 0.7048

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7123 - loss: 0.7047

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7124 - loss: 0.7045

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7125 - loss: 0.7043

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7125 - loss: 0.7042

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7126 - loss: 0.7040

429/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7127 - loss: 0.7039

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7127 - loss: 0.7037

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7128 - loss: 0.7036

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7128 - loss: 0.7035

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7129 - loss: 0.7033

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7130 - loss: 0.7032

459/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7130 - loss: 0.7031

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7131 - loss: 0.7030

473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7131 - loss: 0.7028
Epoch 20: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7132 - loss: 0.7028 - val_accuracy: 0.7038 - val_loss: 0.7000 - learning_rate: 0.0100
Epoch 21/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.5938 - loss: 0.8505

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6697 - loss: 0.7586  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.6994 - loss: 0.7075

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7144 - loss: 0.6771

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7220 - loss: 0.6639

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7256 - loss: 0.6573

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7264 - loss: 0.6570

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7273 - loss: 0.6570

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7270 - loss: 0.6597

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7270 - loss: 0.6621

 49/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7271 - loss: 0.6633

 53/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7271 - loss: 0.6641

 58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7268 - loss: 0.6656

 63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7264 - loss: 0.6673

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7262 - loss: 0.6683

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7259 - loss: 0.6696

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7257 - loss: 0.6702

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7253 - loss: 0.6712

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7251 - loss: 0.6721

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7249 - loss: 0.6728

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7249 - loss: 0.6734

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6740

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6746

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6749

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6752

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6754

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6757

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7248 - loss: 0.6760

133/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7247 - loss: 0.6763

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7247 - loss: 0.6766

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7246 - loss: 0.6768

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7245 - loss: 0.6772

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7244 - loss: 0.6776

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7243 - loss: 0.6779

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7243 - loss: 0.6783

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7242 - loss: 0.6786

173/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7242 - loss: 0.6789

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7241 - loss: 0.6792

183/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7241 - loss: 0.6795

188/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7240 - loss: 0.6798

193/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7239 - loss: 0.6801

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7238 - loss: 0.6804

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7236 - loss: 0.6807

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7235 - loss: 0.6809

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7234 - loss: 0.6812

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7233 - loss: 0.6814

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7232 - loss: 0.6817

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7231 - loss: 0.6819

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7230 - loss: 0.6821

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7229 - loss: 0.6823

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7229 - loss: 0.6825

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6827

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6828

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6829

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6830

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6832

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7225 - loss: 0.6834

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7224 - loss: 0.6836

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6837

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6838

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6839

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6840

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6841

304/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6841

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7221 - loss: 0.6842

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7221 - loss: 0.6843

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7220 - loss: 0.6844

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7220 - loss: 0.6844

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6845

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6845

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7219 - loss: 0.6846

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6846

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6847

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6848

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7218 - loss: 0.6848

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7217 - loss: 0.6848

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6848

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6849

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7217 - loss: 0.6849

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6850

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6850

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7216 - loss: 0.6851

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6851

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6852

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6853

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7215 - loss: 0.6853

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6854

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6854

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6855

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6855

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6856

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7214 - loss: 0.6856
Epoch 21: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7213 - loss: 0.6857 - val_accuracy: 0.7259 - val_loss: 0.6908 - learning_rate: 0.0100
Epoch 22/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.5938 - loss: 0.8932

  4/473 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.6862 - loss: 0.7238  

  7/473 ━━━━━━━━━━━━━━━━━━━━ 9s 20ms/step - accuracy: 0.6974 - loss: 0.7087

 12/473 ━━━━━━━━━━━━━━━━━━━━ 7s 16ms/step - accuracy: 0.7118 - loss: 0.6925

 17/473 ━━━━━━━━━━━━━━━━━━━━ 6s 15ms/step - accuracy: 0.7218 - loss: 0.6747

 22/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7267 - loss: 0.6617

 27/473 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.7302 - loss: 0.6510

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6457

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7317 - loss: 0.6429

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6409

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7315 - loss: 0.6416

 50/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7315 - loss: 0.6417

 55/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7318 - loss: 0.6417

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7319 - loss: 0.6426

 64/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7321 - loss: 0.6435

 69/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7325 - loss: 0.6443

 73/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6453

 78/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6465

 81/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7325 - loss: 0.6472

 86/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7326 - loss: 0.6484

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6493

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6503

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6513

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7328 - loss: 0.6522

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7327 - loss: 0.6531

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7326 - loss: 0.6540

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7324 - loss: 0.6549

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 13ms/step - accuracy: 0.7323 - loss: 0.6555

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7323 - loss: 0.6559

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6563

141/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6568

146/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6572

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7322 - loss: 0.6575

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6579

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6582

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6585

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7321 - loss: 0.6588

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7320 - loss: 0.6591

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7320 - loss: 0.6595

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6599

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6602

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6605

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6608

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7319 - loss: 0.6611

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6614

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6617

221/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7318 - loss: 0.6620

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7317 - loss: 0.6624

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7316 - loss: 0.6628

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6632

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7315 - loss: 0.6636

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6640

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6643

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7314 - loss: 0.6646

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6649

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7313 - loss: 0.6652

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6655

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7312 - loss: 0.6657

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6660

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7311 - loss: 0.6662

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6664

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6666

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7310 - loss: 0.6668

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7310 - loss: 0.6670

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6671

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6672

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6673

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6674

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7309 - loss: 0.6674

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6674

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6675

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6675

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6676

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6676

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7308 - loss: 0.6677

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6677

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6677

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7307 - loss: 0.6678

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7307 - loss: 0.6678

395/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7307 - loss: 0.6679

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6679

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6680

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6680

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6681

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6681

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7306 - loss: 0.6682

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6682

434/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6683

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7305 - loss: 0.6684

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6684

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6685

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7304 - loss: 0.6686

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6687

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6688

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6689
Epoch 22: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7302 - loss: 0.6690 - val_accuracy: 0.7330 - val_loss: 0.6548 - learning_rate: 0.0100
Epoch 23/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.6562 - loss: 0.8340

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6575 - loss: 0.7864  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6615 - loss: 0.7766

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6748 - loss: 0.7523

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6822 - loss: 0.7359

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6898 - loss: 0.7229

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6958 - loss: 0.7128

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.6995 - loss: 0.7062

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7023 - loss: 0.7011

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7053 - loss: 0.6955

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7078 - loss: 0.6910

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7098 - loss: 0.6873

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7112 - loss: 0.6846

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7122 - loss: 0.6824

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7131 - loss: 0.6806

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7139 - loss: 0.6791

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7147 - loss: 0.6780

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7153 - loss: 0.6775

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7161 - loss: 0.6769

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7165 - loss: 0.6766

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7168 - loss: 0.6765

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7171 - loss: 0.6764

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7173 - loss: 0.6765

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7174 - loss: 0.6766

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7177 - loss: 0.6767

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7180 - loss: 0.6766

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7183 - loss: 0.6765

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7187 - loss: 0.6764

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7190 - loss: 0.6762

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7192 - loss: 0.6762

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7194 - loss: 0.6761

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7196 - loss: 0.6761

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7198 - loss: 0.6761

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7200 - loss: 0.6760

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7202 - loss: 0.6760

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7204 - loss: 0.6759

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7205 - loss: 0.6759

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7207 - loss: 0.6758

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7208 - loss: 0.6758

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7209 - loss: 0.6758

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7210 - loss: 0.6758

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7211 - loss: 0.6758

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7212 - loss: 0.6759

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7214 - loss: 0.6759

216/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7215 - loss: 0.6759

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7216 - loss: 0.6759

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7218 - loss: 0.6759

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7218 - loss: 0.6759

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7219 - loss: 0.6760

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7220 - loss: 0.6760

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6760

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7221 - loss: 0.6760

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6760

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7222 - loss: 0.6760

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7223 - loss: 0.6760

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7224 - loss: 0.6759

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7225 - loss: 0.6759

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6759

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7226 - loss: 0.6759

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7227 - loss: 0.6760

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6760

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7228 - loss: 0.6760

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7229 - loss: 0.6761

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7230 - loss: 0.6761

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7230 - loss: 0.6761

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7231 - loss: 0.6762

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7232 - loss: 0.6762

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7232 - loss: 0.6762

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6763

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6764

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7233 - loss: 0.6764

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6765

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6765

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7234 - loss: 0.6766

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6766

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6766

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7235 - loss: 0.6767

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6767

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7236 - loss: 0.6767

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7237 - loss: 0.6767

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7238 - loss: 0.6767

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7239 - loss: 0.6767

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7240 - loss: 0.6767

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.6767

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7242 - loss: 0.6767
Epoch 23: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7242 - loss: 0.6768 - val_accuracy: 0.6779 - val_loss: 0.8059 - learning_rate: 0.0100
Epoch 24/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8125 - loss: 0.4045

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7590 - loss: 0.5475  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7540 - loss: 0.5671

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7467 - loss: 0.5879

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7454 - loss: 0.5960

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7439 - loss: 0.6032

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7412 - loss: 0.6117

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7384 - loss: 0.6201

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7356 - loss: 0.6293

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7345 - loss: 0.6349

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7345 - loss: 0.6379

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7346 - loss: 0.6399

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7347 - loss: 0.6414

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7349 - loss: 0.6422

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7351 - loss: 0.6427

 73/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7353 - loss: 0.6431

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7355 - loss: 0.6433

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7358 - loss: 0.6437

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7360 - loss: 0.6441

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7362 - loss: 0.6444

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7364 - loss: 0.6449

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6454

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6461

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6468

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7366 - loss: 0.6474

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6478

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6482

131/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7369 - loss: 0.6488

136/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7368 - loss: 0.6493

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6498

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6502

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7368 - loss: 0.6506

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7367 - loss: 0.6509

160/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7367 - loss: 0.6512

165/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6516

170/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7366 - loss: 0.6519

175/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6521

180/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7365 - loss: 0.6524

185/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7364 - loss: 0.6526

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7363 - loss: 0.6529

195/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7362 - loss: 0.6531

200/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7361 - loss: 0.6534

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7360 - loss: 0.6536

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7359 - loss: 0.6539

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7358 - loss: 0.6541

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7356 - loss: 0.6543

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7355 - loss: 0.6545

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7355 - loss: 0.6547

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7354 - loss: 0.6549

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7353 - loss: 0.6551

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7352 - loss: 0.6554

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7351 - loss: 0.6556

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7349 - loss: 0.6558

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7348 - loss: 0.6561

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7347 - loss: 0.6563

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7346 - loss: 0.6566

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7345 - loss: 0.6568

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7344 - loss: 0.6571

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7343 - loss: 0.6573

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7342 - loss: 0.6575

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7341 - loss: 0.6577

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7340 - loss: 0.6580

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7339 - loss: 0.6582

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7338 - loss: 0.6585

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7337 - loss: 0.6587

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7336 - loss: 0.6590

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7335 - loss: 0.6592

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7334 - loss: 0.6595

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7333 - loss: 0.6596

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7332 - loss: 0.6597

340/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7332 - loss: 0.6599

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7331 - loss: 0.6600

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7330 - loss: 0.6601

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7330 - loss: 0.6602

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7329 - loss: 0.6603

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7329 - loss: 0.6604

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7328 - loss: 0.6606

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7328 - loss: 0.6607

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7327 - loss: 0.6608

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7327 - loss: 0.6609

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7326 - loss: 0.6610

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7325 - loss: 0.6611

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7325 - loss: 0.6612

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7324 - loss: 0.6613

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7323 - loss: 0.6615

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7323 - loss: 0.6616

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7322 - loss: 0.6617

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7322 - loss: 0.6618

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7321 - loss: 0.6620

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7321 - loss: 0.6621

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7320 - loss: 0.6622

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7320 - loss: 0.6623

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7319 - loss: 0.6624

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7319 - loss: 0.6625

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7318 - loss: 0.6626

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7318 - loss: 0.6627

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7317 - loss: 0.6629
Epoch 24: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7317 - loss: 0.6629 - val_accuracy: 0.7358 - val_loss: 0.6609 - learning_rate: 0.0100
Epoch 25/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7500 - loss: 0.6800

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7105 - loss: 0.6740  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7132 - loss: 0.6721

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7241 - loss: 0.6575

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7298 - loss: 0.6495

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7319 - loss: 0.6477

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7317 - loss: 0.6498

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7318 - loss: 0.6510

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7319 - loss: 0.6516

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7321 - loss: 0.6514

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7322 - loss: 0.6520

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7323 - loss: 0.6524

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7325 - loss: 0.6525

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7327 - loss: 0.6528

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7325 - loss: 0.6536

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7324 - loss: 0.6545

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6555

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7322 - loss: 0.6560

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7320 - loss: 0.6568

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7318 - loss: 0.6576

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7317 - loss: 0.6581

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7314 - loss: 0.6589

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7313 - loss: 0.6594

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7311 - loss: 0.6598

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7310 - loss: 0.6600

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7309 - loss: 0.6604

130/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7307 - loss: 0.6610

135/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7306 - loss: 0.6615

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7305 - loss: 0.6618

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7304 - loss: 0.6623

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7303 - loss: 0.6627

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7302 - loss: 0.6630

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7301 - loss: 0.6634

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7301 - loss: 0.6636

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7300 - loss: 0.6639

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7299 - loss: 0.6642

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7298 - loss: 0.6644

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7297 - loss: 0.6647

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6648

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7296 - loss: 0.6650

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7295 - loss: 0.6652

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7294 - loss: 0.6653

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7293 - loss: 0.6655

214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6656

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6657

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7292 - loss: 0.6658

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6659

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6660

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6661

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7291 - loss: 0.6662

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6663

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6664

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6665

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6665

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6666

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7290 - loss: 0.6667

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6667

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6668

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7289 - loss: 0.6669

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7288 - loss: 0.6670

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7288 - loss: 0.6671

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6673

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6674

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7287 - loss: 0.6675

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6676

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6677

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7286 - loss: 0.6677

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6678

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6679

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7285 - loss: 0.6680

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7284 - loss: 0.6681

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7284 - loss: 0.6682

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6683

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7283 - loss: 0.6684

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6685

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6686

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7282 - loss: 0.6688

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7281 - loss: 0.6689

389/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6690

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6690

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6691

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6692

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6693

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6693

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6694

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6695

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6695

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6696

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6696

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6697

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6697

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6698

464/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6699

472/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6700
Epoch 25: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7280 - loss: 0.6700 - val_accuracy: 0.7008 - val_loss: 0.7168 - learning_rate: 0.0100
Epoch 26/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6875 - loss: 0.5993

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7466 - loss: 0.5960  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7279 - loss: 0.6320

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7238 - loss: 0.6403

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7242 - loss: 0.6406

 27/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7209 - loss: 0.6485

 32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7202 - loss: 0.6524

 37/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7201 - loss: 0.6556

 42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7198 - loss: 0.6579

 47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7197 - loss: 0.6596

 52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7197 - loss: 0.6608

 57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7199 - loss: 0.6614

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7204 - loss: 0.6611

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7208 - loss: 0.6606

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7213 - loss: 0.6599

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7217 - loss: 0.6591

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7221 - loss: 0.6583

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7224 - loss: 0.6577

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7228 - loss: 0.6570

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7232 - loss: 0.6564

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7236 - loss: 0.6559

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7238 - loss: 0.6556

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7241 - loss: 0.6551

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7243 - loss: 0.6549

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7244 - loss: 0.6548

127/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7245 - loss: 0.6546

132/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7246 - loss: 0.6546

137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6545

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7248 - loss: 0.6544

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7249 - loss: 0.6544

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6544

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7250 - loss: 0.6544

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7251 - loss: 0.6544

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7252 - loss: 0.6545

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7252 - loss: 0.6546

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6547

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7253 - loss: 0.6549

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6550

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7254 - loss: 0.6552

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7255 - loss: 0.6553

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7256 - loss: 0.6554

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7257 - loss: 0.6555

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7258 - loss: 0.6556

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6557

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7259 - loss: 0.6559

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6560

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7260 - loss: 0.6562

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7261 - loss: 0.6563

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6564

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7262 - loss: 0.6566

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7263 - loss: 0.6567

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6568

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7264 - loss: 0.6569

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7265 - loss: 0.6569

270/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7266 - loss: 0.6570

275/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7267 - loss: 0.6571

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7267 - loss: 0.6572

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7268 - loss: 0.6573

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7269 - loss: 0.6573

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7270 - loss: 0.6574

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7270 - loss: 0.6575

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6575

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7271 - loss: 0.6576

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6577

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7272 - loss: 0.6578

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6579

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7273 - loss: 0.6580

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6580

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7274 - loss: 0.6581

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6582

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6583

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7275 - loss: 0.6583

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6584

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6585

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7276 - loss: 0.6585

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6586

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6586

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7277 - loss: 0.6587

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6587

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6587

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7278 - loss: 0.6588

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7279 - loss: 0.6588

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7279 - loss: 0.6589

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6589

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6590

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7280 - loss: 0.6590

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6590

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7281 - loss: 0.6591

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6591

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6592

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6592

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7282 - loss: 0.6593

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6593

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6594

467/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7283 - loss: 0.6595
Epoch 26: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7284 - loss: 0.6596 - val_accuracy: 0.6723 - val_loss: 0.7985 - learning_rate: 0.0100
Epoch 27/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7188 - loss: 0.6842

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7296 - loss: 0.6304  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7376 - loss: 0.6214

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7359 - loss: 0.6316

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7376 - loss: 0.6336

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7386 - loss: 0.6333

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7385 - loss: 0.6349

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7372 - loss: 0.6379

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7363 - loss: 0.6404

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7353 - loss: 0.6437

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7341 - loss: 0.6470

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7328 - loss: 0.6501

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7317 - loss: 0.6521

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7307 - loss: 0.6538

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7300 - loss: 0.6550

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7294 - loss: 0.6562

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7290 - loss: 0.6570

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7287 - loss: 0.6575

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7284 - loss: 0.6578

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7283 - loss: 0.6579

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7282 - loss: 0.6578

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7280 - loss: 0.6577

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6576

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7279 - loss: 0.6575

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7277 - loss: 0.6576

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7276 - loss: 0.6578

129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6580

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6579

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7273 - loss: 0.6580

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6579

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6578

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6577

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6576

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6576

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6575

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6574

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7274 - loss: 0.6574

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7275 - loss: 0.6573

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7275 - loss: 0.6573

214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7276 - loss: 0.6572

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7277 - loss: 0.6571

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7277 - loss: 0.6570

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7278 - loss: 0.6570

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7279 - loss: 0.6569

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7280 - loss: 0.6568

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7281 - loss: 0.6567

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7282 - loss: 0.6566

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7283 - loss: 0.6564

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7284 - loss: 0.6563

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7285 - loss: 0.6561

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7286 - loss: 0.6561

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7287 - loss: 0.6560

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7288 - loss: 0.6559

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7289 - loss: 0.6559

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7290 - loss: 0.6558

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7291 - loss: 0.6557

299/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7292 - loss: 0.6557

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7292 - loss: 0.6557

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7293 - loss: 0.6557

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7293 - loss: 0.6557

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6557

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6558

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6558

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7294 - loss: 0.6559

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7295 - loss: 0.6560

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7296 - loss: 0.6559

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7296 - loss: 0.6559

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7297 - loss: 0.6559

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7297 - loss: 0.6558

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6558

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7298 - loss: 0.6557

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7299 - loss: 0.6557

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7299 - loss: 0.6557

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7300 - loss: 0.6557

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7300 - loss: 0.6556

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6556

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6557

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7301 - loss: 0.6557

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6557

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7302 - loss: 0.6558

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6558

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6558

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7303 - loss: 0.6559
Epoch 27: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.
Epoch 27: val_accuracy did not improve from 0.73639

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7304 - loss: 0.6560 - val_accuracy: 0.7275 - val_loss: 0.6787 - learning_rate: 0.0100
Epoch 28/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 51s 109ms/step - accuracy: 0.7812 - loss: 0.5975

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7595 - loss: 0.6545  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7536 - loss: 0.6613

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7491 - loss: 0.6581

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7494 - loss: 0.6543

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7482 - loss: 0.6543

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7492 - loss: 0.6515

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7510 - loss: 0.6473

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7524 - loss: 0.6444

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7532 - loss: 0.6430

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7542 - loss: 0.6408

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7550 - loss: 0.6392

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.6374

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7565 - loss: 0.6360

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7569 - loss: 0.6354

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7573 - loss: 0.6350

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7577 - loss: 0.6346

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7579 - loss: 0.6341

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7581 - loss: 0.6335

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7583 - loss: 0.6328

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7585 - loss: 0.6321

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7588 - loss: 0.6315

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7589 - loss: 0.6311

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7590 - loss: 0.6309

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7591 - loss: 0.6304

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6300

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6298

132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7592 - loss: 0.6296

137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7592 - loss: 0.6294

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7592 - loss: 0.6292

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7591 - loss: 0.6288

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7591 - loss: 0.6285

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6282

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6279

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6276

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6272

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6269

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6266

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6262

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7590 - loss: 0.6260

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Epoch 28: val_accuracy improved from 0.73639 to 0.77175, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7568 - loss: 0.6196 - val_accuracy: 0.7718 - val_loss: 0.5836 - learning_rate: 0.0020
Epoch 29/60
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331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7589 - loss: 0.5993

335/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7590 - loss: 0.5992

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7591 - loss: 0.5990

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7592 - loss: 0.5989

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7593 - loss: 0.5988

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7593 - loss: 0.5986

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7594 - loss: 0.5985

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7595 - loss: 0.5983

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7596 - loss: 0.5982

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7596 - loss: 0.5981

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7597 - loss: 0.5980

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7597 - loss: 0.5979

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7598 - loss: 0.5978

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7599 - loss: 0.5977

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7599 - loss: 0.5976

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7600 - loss: 0.5975

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7600 - loss: 0.5974

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7601 - loss: 0.5973

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7602 - loss: 0.5972

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7602 - loss: 0.5971

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7603 - loss: 0.5970

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7603 - loss: 0.5969

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7604 - loss: 0.5968

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7604 - loss: 0.5967

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7605 - loss: 0.5967

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7605 - loss: 0.5966

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5965

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5965

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7606 - loss: 0.5964
Epoch 29: val_accuracy did not improve from 0.77175

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7607 - loss: 0.5963 - val_accuracy: 0.7621 - val_loss: 0.6008 - learning_rate: 0.0020
Epoch 30/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 1:02 133ms/step - accuracy: 0.6562 - loss: 0.6860

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7345 - loss: 0.5972   

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7429 - loss: 0.5894

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7498 - loss: 0.5843

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7523 - loss: 0.5831

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7521 - loss: 0.5843

 32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.5871

 37/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7521 - loss: 0.5918

 42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7520 - loss: 0.5947

 47/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7525 - loss: 0.5960

 52/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7534 - loss: 0.5962

 57/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7545 - loss: 0.5960

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7554 - loss: 0.5956

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5958

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5960

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7560 - loss: 0.5962

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7560 - loss: 0.5966

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5972

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7559 - loss: 0.5975

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7558 - loss: 0.5976

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5976

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5977

120/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7557 - loss: 0.5978

125/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5978

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7558 - loss: 0.5977

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7559 - loss: 0.5975

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7559 - loss: 0.5975

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7558 - loss: 0.5975

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7557 - loss: 0.5976

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7556 - loss: 0.5978

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7555 - loss: 0.5978

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7554 - loss: 0.5978

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7554 - loss: 0.5979

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7553 - loss: 0.5980

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7552 - loss: 0.5981

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7551 - loss: 0.5983

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7551 - loss: 0.5984

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7550 - loss: 0.5986

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7550 - loss: 0.5986

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5987

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5987

210/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7549 - loss: 0.5988

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7549 - loss: 0.5987

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5987

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5987

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7550 - loss: 0.5986

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7551 - loss: 0.5986

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7551 - loss: 0.5985

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7552 - loss: 0.5984

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7553 - loss: 0.5983

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7554 - loss: 0.5981

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7554 - loss: 0.5980

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7555 - loss: 0.5979

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7556 - loss: 0.5977

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7557 - loss: 0.5976

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7558 - loss: 0.5974

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7559 - loss: 0.5973

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7559 - loss: 0.5972

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7560 - loss: 0.5971

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7561 - loss: 0.5969

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7561 - loss: 0.5968

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5967

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5966

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7562 - loss: 0.5965

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7563 - loss: 0.5965

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7563 - loss: 0.5964

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7564 - loss: 0.5963

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7564 - loss: 0.5962

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7565 - loss: 0.5962

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7565 - loss: 0.5961

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5960

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5959

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7566 - loss: 0.5959

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7567 - loss: 0.5958

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7567 - loss: 0.5957

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7567 - loss: 0.5956

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7568 - loss: 0.5955

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7568 - loss: 0.5954

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7569 - loss: 0.5953

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7569 - loss: 0.5953

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7570 - loss: 0.5952

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7570 - loss: 0.5951

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7570 - loss: 0.5951

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7571 - loss: 0.5950

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7571 - loss: 0.5950

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7571 - loss: 0.5950

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Epoch 30: val_accuracy improved from 0.77175 to 0.77597, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7573 - loss: 0.5949 - val_accuracy: 0.7760 - val_loss: 0.5733 - learning_rate: 0.0020
Epoch 31/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.7188 - loss: 0.6817

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224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7570 - loss: 0.5940

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Epoch 31: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7596 - loss: 0.5899 - val_accuracy: 0.7748 - val_loss: 0.5714 - learning_rate: 0.0020
Epoch 32/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 105ms/step - accuracy: 0.9375 - loss: 0.4048

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 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7730 - loss: 0.5667

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7732 - loss: 0.5669

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5670

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5674

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7734 - loss: 0.5678

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7732 - loss: 0.5684

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7728 - loss: 0.5692

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5696

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7726 - loss: 0.5698

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5699

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5699

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5701

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7725 - loss: 0.5702

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7724 - loss: 0.5704

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7724 - loss: 0.5706

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7723 - loss: 0.5709

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7722 - loss: 0.5712

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7721 - loss: 0.5716

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5719

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5721

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7720 - loss: 0.5724

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5727

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5729

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5730

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5732

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5733

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7719 - loss: 0.5735

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5737

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5738

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7718 - loss: 0.5739

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5740

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5741

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5743

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5744

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5744

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5745

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5745

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5745

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5744

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5744

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5745

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5745

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5746

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5746

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7715 - loss: 0.5747

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5747

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5748

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7714 - loss: 0.5749

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7713 - loss: 0.5750

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7713 - loss: 0.5751

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7712 - loss: 0.5752

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7712 - loss: 0.5753

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5753

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5754

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7711 - loss: 0.5755

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7710 - loss: 0.5755

387/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7710 - loss: 0.5756

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7709 - loss: 0.5757

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7708 - loss: 0.5758

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7708 - loss: 0.5759

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5759

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5760

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7707 - loss: 0.5760

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7706 - loss: 0.5761

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7706 - loss: 0.5761

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5762

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5763

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7705 - loss: 0.5763

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7704 - loss: 0.5763

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7704 - loss: 0.5764

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7704 - loss: 0.5765
Epoch 32: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7703 - loss: 0.5765 - val_accuracy: 0.7728 - val_loss: 0.5806 - learning_rate: 0.0020
Epoch 33/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 54s 116ms/step - accuracy: 0.7812 - loss: 0.5685

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8049 - loss: 0.5255  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8066 - loss: 0.5192

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7994 - loss: 0.5301

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7946 - loss: 0.5398

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7907 - loss: 0.5470

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7889 - loss: 0.5497

 36/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7866 - loss: 0.5524

 41/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7846 - loss: 0.5546

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7832 - loss: 0.5556

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7826 - loss: 0.5558

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7822 - loss: 0.5561

 57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7818 - loss: 0.5567

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7811 - loss: 0.5577

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7803 - loss: 0.5591

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7796 - loss: 0.5602

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7791 - loss: 0.5607

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7788 - loss: 0.5610

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7786 - loss: 0.5610

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7784 - loss: 0.5611

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7781 - loss: 0.5613

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7777 - loss: 0.5616

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7773 - loss: 0.5619

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7768 - loss: 0.5623

117/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7764 - loss: 0.5627

122/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7761 - loss: 0.5630

127/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7757 - loss: 0.5633

132/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7754 - loss: 0.5637

137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7750 - loss: 0.5642

142/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7747 - loss: 0.5645

147/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7745 - loss: 0.5649

152/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7743 - loss: 0.5651

157/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7741 - loss: 0.5653

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7739 - loss: 0.5655

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7737 - loss: 0.5657

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7735 - loss: 0.5658

178/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7733 - loss: 0.5659

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5660

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7729 - loss: 0.5662

190/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7728 - loss: 0.5663

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7727 - loss: 0.5663

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7725 - loss: 0.5664

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7723 - loss: 0.5666

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7722 - loss: 0.5667

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7721 - loss: 0.5668

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7719 - loss: 0.5670

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7718 - loss: 0.5671

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7717 - loss: 0.5673

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7716 - loss: 0.5674

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7715 - loss: 0.5675

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7714 - loss: 0.5676

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7713 - loss: 0.5677

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7712 - loss: 0.5679

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7711 - loss: 0.5681

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7711 - loss: 0.5682

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7710 - loss: 0.5683

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7710 - loss: 0.5685

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7709 - loss: 0.5686

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7708 - loss: 0.5687

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7708 - loss: 0.5688

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7707 - loss: 0.5689

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7707 - loss: 0.5690

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7706 - loss: 0.5691

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7706 - loss: 0.5693

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7705 - loss: 0.5695

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7704 - loss: 0.5697

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7704 - loss: 0.5698

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7703 - loss: 0.5700

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7702 - loss: 0.5701

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7702 - loss: 0.5703

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7701 - loss: 0.5704

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7701 - loss: 0.5705

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5707

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5708

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7700 - loss: 0.5709

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7699 - loss: 0.5710

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7699 - loss: 0.5711

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5712

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5713

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7698 - loss: 0.5713

390/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5714

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5714

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5715

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5716

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5716

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5717

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5717

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5718

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5719

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5719

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5720

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7695 - loss: 0.5721

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5721

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5722

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5722

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5723

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7694 - loss: 0.5723
Epoch 33: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7694 - loss: 0.5724 - val_accuracy: 0.7683 - val_loss: 0.5852 - learning_rate: 0.0020
Epoch 34/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 102ms/step - accuracy: 0.9062 - loss: 0.5441

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8330 - loss: 0.5340  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.8097 - loss: 0.5601

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7990 - loss: 0.5729

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7934 - loss: 0.5746

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7898 - loss: 0.5757

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7871 - loss: 0.5765

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7857 - loss: 0.5761

 42/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7845 - loss: 0.5762

 46/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7839 - loss: 0.5760

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7837 - loss: 0.5749

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7835 - loss: 0.5738

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7832 - loss: 0.5731

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5727

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5720

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5715

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7831 - loss: 0.5712

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7831 - loss: 0.5707

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5703

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5702

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7825 - loss: 0.5702

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7823 - loss: 0.5702

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5701

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7818 - loss: 0.5699

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7816 - loss: 0.5697

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7814 - loss: 0.5696

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7811 - loss: 0.5696

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7809 - loss: 0.5696

137/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7806 - loss: 0.5696

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7803 - loss: 0.5697

145/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7801 - loss: 0.5697

150/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7798 - loss: 0.5696

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7796 - loss: 0.5696

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7794 - loss: 0.5696

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7792 - loss: 0.5696

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7790 - loss: 0.5696

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7788 - loss: 0.5696

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7786 - loss: 0.5697

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7783 - loss: 0.5697

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7781 - loss: 0.5698

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7780 - loss: 0.5698

198/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5698

203/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7777 - loss: 0.5699

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7775 - loss: 0.5699

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7774 - loss: 0.5700

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7772 - loss: 0.5700

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7771 - loss: 0.5701

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7769 - loss: 0.5703

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7768 - loss: 0.5704

237/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7766 - loss: 0.5705

242/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7765 - loss: 0.5706

247/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7763 - loss: 0.5707

252/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7762 - loss: 0.5708

257/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7761 - loss: 0.5709

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7759 - loss: 0.5710

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7758 - loss: 0.5711

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7757 - loss: 0.5711

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7757 - loss: 0.5711

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7756 - loss: 0.5712

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7756 - loss: 0.5712

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7755 - loss: 0.5712

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7754 - loss: 0.5713

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7754 - loss: 0.5713

307/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7754 - loss: 0.5713

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7753 - loss: 0.5714

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7753 - loss: 0.5714

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7752 - loss: 0.5714

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7752 - loss: 0.5715

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7751 - loss: 0.5715

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7750 - loss: 0.5715

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7749 - loss: 0.5716

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7749 - loss: 0.5716

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7748 - loss: 0.5716

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5716

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5715

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7748 - loss: 0.5715

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7747 - loss: 0.5715

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7746 - loss: 0.5716

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5717

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5717

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7745 - loss: 0.5718

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5718

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5718

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7744 - loss: 0.5719
Epoch 34: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7743 - loss: 0.5719 - val_accuracy: 0.7517 - val_loss: 0.6262 - learning_rate: 0.0020
Epoch 35/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.7188 - loss: 0.5507

  5/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7626 - loss: 0.5096  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7751 - loss: 0.5021

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7801 - loss: 0.5036

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7823 - loss: 0.5072

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7826 - loss: 0.5126

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7815 - loss: 0.5194

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7804 - loss: 0.5248

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7796 - loss: 0.5290

 45/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7790 - loss: 0.5322

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7785 - loss: 0.5346

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7779 - loss: 0.5372

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7773 - loss: 0.5396

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7769 - loss: 0.5417

 70/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7765 - loss: 0.5439

 75/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7761 - loss: 0.5458

 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7755 - loss: 0.5481

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7747 - loss: 0.5505

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7740 - loss: 0.5526

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7734 - loss: 0.5544

100/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7730 - loss: 0.5559

105/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7729 - loss: 0.5570

110/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7729 - loss: 0.5576

115/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7729 - loss: 0.5583

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7729 - loss: 0.5588

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7730 - loss: 0.5591

129/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5594

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5598

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5602

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5604

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5606

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7730 - loss: 0.5608

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5610

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5611

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7731 - loss: 0.5612

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7732 - loss: 0.5614

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7732 - loss: 0.5614

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7733 - loss: 0.5614

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7734 - loss: 0.5614

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7735 - loss: 0.5613

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7737 - loss: 0.5612

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.7738 - loss: 0.5611

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7739 - loss: 0.5610

213/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7740 - loss: 0.5610

218/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7740 - loss: 0.5610

223/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7741 - loss: 0.5610

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7741 - loss: 0.5610

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7742 - loss: 0.5610

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5609

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5609

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5608

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7743 - loss: 0.5608

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5607

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5606

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5606

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7745 - loss: 0.5605

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7744 - loss: 0.5604

301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5605

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5605

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7744 - loss: 0.5604

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7743 - loss: 0.5604

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7742 - loss: 0.5604

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7742 - loss: 0.5604

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5605

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5606

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7741 - loss: 0.5607

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7740 - loss: 0.5608

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7740 - loss: 0.5608

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7739 - loss: 0.5609

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7739 - loss: 0.5610

386/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7739 - loss: 0.5611

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5611

394/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5611

399/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5612

404/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7739 - loss: 0.5612

409/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5613

414/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5613

419/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5614

424/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5614

428/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7738 - loss: 0.5615

433/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5616

438/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5617

443/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5617

448/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5618

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5619

458/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5619

463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7736 - loss: 0.5620

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7736 - loss: 0.5621
Epoch 35: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7735 - loss: 0.5621 - val_accuracy: 0.7744 - val_loss: 0.5716 - learning_rate: 0.0020
Epoch 36/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 106ms/step - accuracy: 0.7812 - loss: 0.5845

  5/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.8071 - loss: 0.5976  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7929 - loss: 0.5918

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7875 - loss: 0.5906

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7822 - loss: 0.5917

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7781 - loss: 0.5922

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7742 - loss: 0.5930

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7721 - loss: 0.5924

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7707 - loss: 0.5913

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7699 - loss: 0.5902

 48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7696 - loss: 0.5892

 53/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7689 - loss: 0.5886

 58/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7683 - loss: 0.5883

 63/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7678 - loss: 0.5877

 68/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7674 - loss: 0.5868

 73/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5860

 78/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5852

 83/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7671 - loss: 0.5847

 88/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7671 - loss: 0.5842

 93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7672 - loss: 0.5838

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7674 - loss: 0.5833

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7675 - loss: 0.5830

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7677 - loss: 0.5826

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7679 - loss: 0.5821

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7681 - loss: 0.5817

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7682 - loss: 0.5813

128/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7684 - loss: 0.5810

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7685 - loss: 0.5806

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7686 - loss: 0.5803

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7686 - loss: 0.5801

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7687 - loss: 0.5799

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7687 - loss: 0.5798

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7688 - loss: 0.5795

163/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7688 - loss: 0.5792

168/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5790

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5789

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5787

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7689 - loss: 0.5786

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5784

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5783

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5782

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5782

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7691 - loss: 0.5781

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5781

215/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7690 - loss: 0.5780

220/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5780

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5779

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5779

233/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5778

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5777

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5777

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7690 - loss: 0.5775

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5774

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5773

262/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7691 - loss: 0.5772

267/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5770

272/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5769

277/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5768

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5767

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5767

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5766

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5766

301/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7692 - loss: 0.5765

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5765

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5764

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5763

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5762

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5761

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7693 - loss: 0.5761

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5760

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5759

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5758

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5757

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5756

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7694 - loss: 0.5755

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7695 - loss: 0.5754

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7695 - loss: 0.5753

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5751

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5750

386/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7696 - loss: 0.5749

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7696 - loss: 0.5748

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5747

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5746

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5745

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7697 - loss: 0.5744

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5744

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5743

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5742

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7698 - loss: 0.5741

436/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5741

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5740

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5740

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5739

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7699 - loss: 0.5739

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7700 - loss: 0.5738

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7700 - loss: 0.5737
Epoch 36: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.
Epoch 36: val_accuracy did not improve from 0.77597

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7700 - loss: 0.5736 - val_accuracy: 0.7691 - val_loss: 0.5893 - learning_rate: 0.0020
Epoch 37/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 100ms/step - accuracy: 0.6250 - loss: 0.9760

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7396 - loss: 0.7031  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7486 - loss: 0.6541

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7510 - loss: 0.6334

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7540 - loss: 0.6194

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7574 - loss: 0.6089

 32/473 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.7595 - loss: 0.6003

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7608 - loss: 0.5957

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7619 - loss: 0.5919

 44/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7629 - loss: 0.5890

 48/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7634 - loss: 0.5863

 52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7640 - loss: 0.5841

 56/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7642 - loss: 0.5826

 60/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7644 - loss: 0.5811

 64/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7648 - loss: 0.5793

 68/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7653 - loss: 0.5776

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Epoch 37: val_accuracy improved from 0.77597 to 0.77718, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7754 - loss: 0.5516 - val_accuracy: 0.7772 - val_loss: 0.5747 - learning_rate: 4.0000e-04
Epoch 38/60
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Epoch 38: val_accuracy improved from 0.77718 to 0.77959, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.7851 - loss: 0.5388 - val_accuracy: 0.7796 - val_loss: 0.5663 - learning_rate: 4.0000e-04
Epoch 39/60
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322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7870 - loss: 0.5344

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7869 - loss: 0.5346

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7867 - loss: 0.5348

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7866 - loss: 0.5350

342/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7865 - loss: 0.5351

347/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7864 - loss: 0.5353

352/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7863 - loss: 0.5354

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7862 - loss: 0.5356

362/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7861 - loss: 0.5357

367/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7860 - loss: 0.5358

372/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7859 - loss: 0.5360

377/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7858 - loss: 0.5361

382/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7857 - loss: 0.5362

387/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7856 - loss: 0.5363

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7855 - loss: 0.5364

397/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7855 - loss: 0.5365

402/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7854 - loss: 0.5366

407/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7853 - loss: 0.5367

412/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7852 - loss: 0.5368

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7852 - loss: 0.5369

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7851 - loss: 0.5370

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7850 - loss: 0.5370

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7850 - loss: 0.5371

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5372

442/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5372

447/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5373

452/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5374

457/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7846 - loss: 0.5375

462/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7846 - loss: 0.5376

468/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7845 - loss: 0.5376
Epoch 39: val_accuracy did not improve from 0.77959

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7844 - loss: 0.5377 - val_accuracy: 0.7780 - val_loss: 0.5734 - learning_rate: 4.0000e-04
Epoch 40/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.6875 - loss: 0.9082

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7243 - loss: 0.7213  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7514 - loss: 0.6566

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7626 - loss: 0.6235

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - accuracy: 0.7689 - loss: 0.6036

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7708 - loss: 0.5943

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7726 - loss: 0.5864

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7742 - loss: 0.5808

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7759 - loss: 0.5753

 46/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7776 - loss: 0.5703

 51/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7790 - loss: 0.5663

 56/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7802 - loss: 0.5627

 61/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7809 - loss: 0.5605

 66/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7816 - loss: 0.5584

 71/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5568

 76/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7824 - loss: 0.5553

 81/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5538

 86/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5528

 91/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7830 - loss: 0.5522

 96/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5519

101/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7829 - loss: 0.5515

106/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7828 - loss: 0.5513

111/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5511

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5507

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5502

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7827 - loss: 0.5497

131/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7827 - loss: 0.5492

136/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5486

141/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5482

146/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5479

151/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5475

156/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5472

161/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7829 - loss: 0.5468

166/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5464

171/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5460

176/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5458

181/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5456

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5453

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7831 - loss: 0.5451

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5449

201/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5447

206/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5446

211/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5445

216/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7832 - loss: 0.5443

221/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7831 - loss: 0.5443

226/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7831 - loss: 0.5442

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7831 - loss: 0.5442

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7830 - loss: 0.5441

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7829 - loss: 0.5441

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7828 - loss: 0.5441

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7828 - loss: 0.5441

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7827 - loss: 0.5442

281/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443

286/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443

291/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7826 - loss: 0.5443

296/473 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.7825 - loss: 0.5444

301/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7825 - loss: 0.5444

306/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7825 - loss: 0.5445

311/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

316/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

321/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

326/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

331/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

336/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7824 - loss: 0.5445

346/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446

356/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446

361/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5446

366/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445

371/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445

376/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5445

381/473 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7823 - loss: 0.5444

386/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5444

391/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5444

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5443

401/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5443

406/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5442

411/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5442

416/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5442

421/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5441

426/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5441

431/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5441

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5441

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5440

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5440

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5440

454/473 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7823 - loss: 0.5440

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Epoch 40: val_accuracy improved from 0.77959 to 0.78139, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7823 - loss: 0.5440 - val_accuracy: 0.7814 - val_loss: 0.5636 - learning_rate: 4.0000e-04
Epoch 41/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 53s 112ms/step - accuracy: 0.8125 - loss: 0.4921

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463/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7784 - loss: 0.5504

470/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7784 - loss: 0.5504
Epoch 41: val_accuracy did not improve from 0.78139

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7784 - loss: 0.5503 - val_accuracy: 0.7768 - val_loss: 0.5727 - learning_rate: 4.0000e-04
Epoch 42/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 104ms/step - accuracy: 0.6562 - loss: 0.6868

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 80/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7792 - loss: 0.5250

 85/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7796 - loss: 0.5249

 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7801 - loss: 0.5247

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7805 - loss: 0.5246

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7808 - loss: 0.5247

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7810 - loss: 0.5246

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7813 - loss: 0.5245

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7815 - loss: 0.5245

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7818 - loss: 0.5245

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7821 - loss: 0.5244

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7823 - loss: 0.5244

133/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7825 - loss: 0.5244

138/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7828 - loss: 0.5244

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7830 - loss: 0.5245

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7832 - loss: 0.5245

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7834 - loss: 0.5246

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7836 - loss: 0.5246

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7837 - loss: 0.5246

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7838 - loss: 0.5246

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7839 - loss: 0.5247

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7840 - loss: 0.5247

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7841 - loss: 0.5247

186/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7842 - loss: 0.5247

191/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7843 - loss: 0.5247

196/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5247

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5247

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5248

205/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7844 - loss: 0.5248

208/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5248

213/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5248

218/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7845 - loss: 0.5249

223/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7846 - loss: 0.5250

228/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7846 - loss: 0.5250

232/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250

240/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7847 - loss: 0.5250

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7848 - loss: 0.5250

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7848 - loss: 0.5249

255/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249

260/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249

265/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7849 - loss: 0.5249

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248

282/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5248

287/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5249

292/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5249

297/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5250

302/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5250

307/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5251

312/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5252

317/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5252

322/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5253

327/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5254

332/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7851 - loss: 0.5255

337/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5256

341/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5257

345/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5257

350/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5258

355/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5259

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5260

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5261

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5262

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5263

380/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5264

385/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5265

389/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5265

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5266

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5267

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5268

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5268

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5269

418/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5270

423/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5271

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5272

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5273

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5274

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5275

446/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5276

451/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5277

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5278

461/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5280

466/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7847 - loss: 0.5281
Epoch 42: val_accuracy did not improve from 0.78139

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7847 - loss: 0.5283 - val_accuracy: 0.7728 - val_loss: 0.5732 - learning_rate: 4.0000e-04
Epoch 43/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 47s 101ms/step - accuracy: 0.9062 - loss: 0.3541

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8389 - loss: 0.4537  

 11/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8196 - loss: 0.4786

 16/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8077 - loss: 0.4943

 21/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4990

 26/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8019 - loss: 0.4991

 31/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8004 - loss: 0.5003

 36/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7992 - loss: 0.5014

 41/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7981 - loss: 0.5027

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7976 - loss: 0.5035

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7969 - loss: 0.5048

 55/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7960 - loss: 0.5062

 60/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7953 - loss: 0.5073

 65/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7946 - loss: 0.5083

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7941 - loss: 0.5092

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7934 - loss: 0.5105

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7927 - loss: 0.5116

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7923 - loss: 0.5126

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7919 - loss: 0.5135

 93/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7917 - loss: 0.5140

 98/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7916 - loss: 0.5146

103/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7912 - loss: 0.5154

108/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7908 - loss: 0.5161

113/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7905 - loss: 0.5169

118/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7901 - loss: 0.5176

123/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7897 - loss: 0.5184

128/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7894 - loss: 0.5191

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7889 - loss: 0.5199

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7886 - loss: 0.5205

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7883 - loss: 0.5212

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7880 - loss: 0.5219

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7877 - loss: 0.5226

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7873 - loss: 0.5234

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7871 - loss: 0.5240

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7869 - loss: 0.5247

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7867 - loss: 0.5252

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7865 - loss: 0.5258

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7863 - loss: 0.5263

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7862 - loss: 0.5268

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7860 - loss: 0.5273

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7859 - loss: 0.5278

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7858 - loss: 0.5281

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7857 - loss: 0.5285

214/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7857 - loss: 0.5288

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7856 - loss: 0.5291

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7856 - loss: 0.5294

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7855 - loss: 0.5297

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7855 - loss: 0.5299

239/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7854 - loss: 0.5301

244/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7854 - loss: 0.5304

249/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7853 - loss: 0.5306

254/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7853 - loss: 0.5309

259/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7852 - loss: 0.5311

264/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7852 - loss: 0.5313

269/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5315

274/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5317

279/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7851 - loss: 0.5319

284/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5321

289/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5323

294/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5325

299/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7850 - loss: 0.5326

304/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5328

309/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5329

314/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5331

319/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7850 - loss: 0.5332

324/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5334

329/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5335

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5337

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5338

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5339

349/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5340

354/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5342

359/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5343

364/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5344

369/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5345

374/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5346

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5346

384/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7849 - loss: 0.5347

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5348

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5349

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7849 - loss: 0.5350

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5350

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5351

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5352

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5353

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5353

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5354

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5355

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5355

440/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5356

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5356

449/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357

453/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357

456/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5357

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5358

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7848 - loss: 0.5358
Epoch 43: val_accuracy did not improve from 0.78139

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7848 - loss: 0.5359 - val_accuracy: 0.7780 - val_loss: 0.5715 - learning_rate: 4.0000e-04
Epoch 44/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.8438 - loss: 0.4009

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7701 - loss: 0.4954  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7623 - loss: 0.5212

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7585 - loss: 0.5332

 19/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7581 - loss: 0.5386

 23/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7598 - loss: 0.5392

 27/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7611 - loss: 0.5401

 32/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7636 - loss: 0.5406

 37/473 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.7653 - loss: 0.5420

 42/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7673 - loss: 0.5413

 47/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7688 - loss: 0.5413

 52/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7699 - loss: 0.5420

 57/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.7707 - loss: 0.5420

 62/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7718 - loss: 0.5414

 67/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7727 - loss: 0.5410

 72/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7733 - loss: 0.5410

 77/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7736 - loss: 0.5415

 82/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7738 - loss: 0.5422

 87/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7739 - loss: 0.5427

 92/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7741 - loss: 0.5433

 97/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7744 - loss: 0.5435

102/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7747 - loss: 0.5434

107/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7751 - loss: 0.5431

112/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7755 - loss: 0.5429

116/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7757 - loss: 0.5427

121/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7759 - loss: 0.5426

126/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7762 - loss: 0.5424

130/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7764 - loss: 0.5424

134/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7765 - loss: 0.5423

139/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7768 - loss: 0.5422

143/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7769 - loss: 0.5421

148/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7771 - loss: 0.5421

153/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7773 - loss: 0.5421

158/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7774 - loss: 0.5420

162/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7775 - loss: 0.5420

167/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7777 - loss: 0.5420

172/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5420

177/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7778 - loss: 0.5420

182/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7779 - loss: 0.5419

187/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7781 - loss: 0.5418

192/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7782 - loss: 0.5417

197/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7783 - loss: 0.5417

202/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7784 - loss: 0.5417

207/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7785 - loss: 0.5416

212/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7786 - loss: 0.5416

217/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7787 - loss: 0.5415

222/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7788 - loss: 0.5413

227/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7790 - loss: 0.5412

231/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7790 - loss: 0.5411

236/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7792 - loss: 0.5409

241/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7793 - loss: 0.5407

246/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7794 - loss: 0.5406

251/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7794 - loss: 0.5405

256/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7795 - loss: 0.5403

261/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7796 - loss: 0.5402

266/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7797 - loss: 0.5401

271/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7798 - loss: 0.5401

276/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7798 - loss: 0.5400

280/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7799 - loss: 0.5400

285/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7800 - loss: 0.5399

290/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7800 - loss: 0.5399

295/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7801 - loss: 0.5398

300/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7802 - loss: 0.5397

305/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7803 - loss: 0.5396

310/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7803 - loss: 0.5396

315/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7804 - loss: 0.5395

320/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7805 - loss: 0.5394

325/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7806 - loss: 0.5393

330/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7806 - loss: 0.5393

334/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7807 - loss: 0.5392

339/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7807 - loss: 0.5391

344/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7808 - loss: 0.5391

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7809 - loss: 0.5390

351/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7809 - loss: 0.5390

357/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389

360/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389

365/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7810 - loss: 0.5389

370/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7811 - loss: 0.5388

375/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7811 - loss: 0.5388

379/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5388

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5388

388/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7812 - loss: 0.5387

393/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387

398/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387

403/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7813 - loss: 0.5387

408/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387

413/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387

417/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387

422/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387

427/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7814 - loss: 0.5387

432/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5387

437/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386

441/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386

445/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386

450/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7815 - loss: 0.5386

455/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386

460/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386

465/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7816 - loss: 0.5386
Epoch 44: val_accuracy did not improve from 0.78139

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7816 - loss: 0.5386 - val_accuracy: 0.7812 - val_loss: 0.5683 - learning_rate: 4.0000e-04
Epoch 45/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 49s 104ms/step - accuracy: 0.8125 - loss: 0.4872

  6/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8039 - loss: 0.5049  

 10/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8007 - loss: 0.4998

 15/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8014 - loss: 0.4949

 20/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8028 - loss: 0.4934

 25/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4938

 30/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8030 - loss: 0.4960

 35/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8033 - loss: 0.4969

 40/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8035 - loss: 0.4983

 45/473 ━━━━━━━━━━━━━━━━━━━━ 5s 12ms/step - accuracy: 0.8030 - loss: 0.5000

 50/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8021 - loss: 0.5027

 54/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8016 - loss: 0.5045

 59/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8012 - loss: 0.5063

 64/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8009 - loss: 0.5075

 69/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8007 - loss: 0.5085

 74/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8005 - loss: 0.5095

 79/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8001 - loss: 0.5107

 84/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7997 - loss: 0.5117

 89/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7993 - loss: 0.5125

 94/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7987 - loss: 0.5134

 99/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7981 - loss: 0.5147

104/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7975 - loss: 0.5160

109/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7968 - loss: 0.5171

114/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7962 - loss: 0.5180

119/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7958 - loss: 0.5187

124/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7954 - loss: 0.5191

129/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7951 - loss: 0.5195

134/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7948 - loss: 0.5197

139/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7945 - loss: 0.5200

144/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7942 - loss: 0.5202

149/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7939 - loss: 0.5206

154/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7936 - loss: 0.5210

159/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7934 - loss: 0.5212

164/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7932 - loss: 0.5214

169/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7931 - loss: 0.5216

174/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7931 - loss: 0.5216

179/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7930 - loss: 0.5217

184/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7929 - loss: 0.5217

189/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7928 - loss: 0.5218

194/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7928 - loss: 0.5219

199/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7927 - loss: 0.5221

204/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7927 - loss: 0.5223

209/473 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.7926 - loss: 0.5225

214/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5226

219/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5228

224/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7925 - loss: 0.5229

229/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5230

234/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5230

238/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7924 - loss: 0.5232

243/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7923 - loss: 0.5233

248/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7922 - loss: 0.5234

253/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7922 - loss: 0.5236

258/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7921 - loss: 0.5237

263/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7921 - loss: 0.5238

268/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7920 - loss: 0.5239

273/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7920 - loss: 0.5240

278/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5241

283/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5242

288/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7919 - loss: 0.5243

293/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7918 - loss: 0.5245

298/473 ━━━━━━━━━━━━━━━━━━━━ 2s 12ms/step - accuracy: 0.7917 - loss: 0.5246

303/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7917 - loss: 0.5247

308/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7916 - loss: 0.5249

313/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7915 - loss: 0.5250

318/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7915 - loss: 0.5252

323/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7914 - loss: 0.5253

328/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7913 - loss: 0.5255

333/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7913 - loss: 0.5257

338/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7912 - loss: 0.5259

343/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7911 - loss: 0.5260

348/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7911 - loss: 0.5262

353/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7910 - loss: 0.5264

358/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5265

363/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5267

368/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7909 - loss: 0.5268

373/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7908 - loss: 0.5269

378/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7908 - loss: 0.5271

383/473 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7907 - loss: 0.5272

388/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5273

392/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5274

396/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7907 - loss: 0.5274

400/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7906 - loss: 0.5275

405/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7906 - loss: 0.5276

410/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7905 - loss: 0.5277

415/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7905 - loss: 0.5279

420/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7905 - loss: 0.5280

425/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7904 - loss: 0.5281

430/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7904 - loss: 0.5282

435/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7903 - loss: 0.5283

439/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7903 - loss: 0.5284

444/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7903 - loss: 0.5285

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Epoch 45: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.
Epoch 45: val_accuracy did not improve from 0.78139

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7901 - loss: 0.5289 - val_accuracy: 0.7798 - val_loss: 0.5694 - learning_rate: 4.0000e-04
Epoch 46/60
  1/473 ━━━━━━━━━━━━━━━━━━━━ 48s 103ms/step - accuracy: 0.8438 - loss: 0.3964

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Epoch 46: val_accuracy improved from 0.78139 to 0.78220, saving model to /home/iamtxena/sandbox/mit-ai/capstone/Facial_Emotion_Recognition/results/best_model_complex_20240411-003526.keras

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7880 - loss: 0.5354 - val_accuracy: 0.7822 - val_loss: 0.5676 - learning_rate: 8.0000e-05
Epoch 47/60
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 90/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7569 - loss: 0.5835

 95/473 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.7577 - loss: 0.5819

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473/473 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7737 - loss: 0.5524
Epoch 47: val_accuracy did not improve from 0.78220

473/473 ━━━━━━━━━━━━━━━━━━━━ 6s 13ms/step - accuracy: 0.7737 - loss: 0.5523 - val_accuracy: 0.7818 - val_loss: 0.5682 - learning_rate: 8.0000e-05
Epoch 47: early stopping
Restoring model weights from the end of the best epoch: 40.

Plotting the Training and Validation Accuracies¶

In [64]:
plt.plot(history_complex.history["accuracy"])
plt.plot(history_complex.history["val_accuracy"])
plt.title("Complex CNN Model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()
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Evaluating the Model on Test Set¶

In [65]:
# Calculate the number of steps for the entire test set to be processed
test_steps = test_generator.samples // batch_size

# If the number of samples isn't a multiple of the batch size,
# you have one more batch with the remaining samples
if test_generator.samples % batch_size > 0:
    test_steps += 1

# Evaluating the model on the test set
evaluation_results = model_complex.evaluate(test_generator, steps=test_steps)
print(f"Loss: {evaluation_results[0]}, Accuracy: {evaluation_results[1]}")
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.8750 - loss: 0.3037

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.8281 - loss: 0.4269 
Loss: 0.46025848388671875, Accuracy: 0.8125

Plotting Confusion Matrix¶

In [66]:
pred_probabilities = model_complex.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("Complex CNN Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 453ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step  
              precision    recall  f1-score   support

       happy       0.93      0.88      0.90        32
     neutral       0.69      0.84      0.76        32
         sad       0.74      0.62      0.68        32
    surprise       0.91      0.91      0.91        32

    accuracy                           0.81       128
   macro avg       0.82      0.81      0.81       128
weighted avg       0.82      0.81      0.81       128

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Observations and Insights:

  • The complex CNN model contains 1,096,452 total parameters with 1,094,148 being trainable. This represents an increment in complexity compared to the previous custom CNN models.
  • With an accuracy of 81.25% on the test set, this model has outperformed the previous two custom CNN models, reflecting its ability to generalize better to new data.
  • The confusion matrix presents strong performance in recognizing 'happy' and 'surprise' emotions with f1-scores of 0.90 and 0.91, respectively, similar to the other models, but with better results for 'neutral' and 'sad' emotions with f1-scores of 0.76 and 0.68.
  • Overall, this model has shown a balanced performance across different emotions and suggests that increasing the complexity with an additional convolutional layer has provided benefits in learning facial emotion features.
  • We have tested this model with a dedicated set of hyperparameters batch of tests (experimenting with different learning rates, batch sizes, optimizers) reaching up to 85% of accuracy. However, when trying to reproduce the accuracy with the best parameters, we still got this 81% (great but still a hard line to improve).

Plotting the Confusion Matrix for the chosen final model¶

In [67]:
pred_probabilities = model_complex.predict(test_generator, steps=test_steps)
pred = np.argmax(pred_probabilities, axis=1)

# Getting the true labels from the generator
y_true = test_generator.classes

# Printing the classification report with actual emotion labels
print(classification_report(y_true, pred, target_names=CATEGORIES))

# Plotting the heatmap using confusion matrix
cm = confusion_matrix(y_true, pred)
plt.figure(figsize=(8, 5))
sns.heatmap(cm, annot=True, fmt=".0f", xticklabels=CATEGORIES, yticklabels=CATEGORIES)
plt.title("Best Model Confusion Matrix")
plt.ylabel("Actual")
plt.xlabel("Predicted")
plt.show()
1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step 
              precision    recall  f1-score   support

       happy       0.93      0.88      0.90        32
     neutral       0.69      0.84      0.76        32
         sad       0.74      0.62      0.68        32
    surprise       0.91      0.91      0.91        32

    accuracy                           0.81       128
   macro avg       0.82      0.81      0.81       128
weighted avg       0.82      0.81      0.81       128

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Observations and Insights:

  • The model is highly effective at correctly identifying 'happy' and 'surprise' emotions with 28 and 29 correct predictions respectively, out of 32 instances each, indicating strong precision and recall for these classes.
  • It performs well on 'neutral' emotion too, with 27 correct predictions, but has some misclassifications as 'sad'.
  • The 'sad' emotion is also well-recognized with 20 correct predictions; however, there are 10 instances where 'sad' was incorrectly predicted as 'neutral'.
  • The model shows very few instances of confusion between 'happy' and 'sad' or 'surprise', which suggests distinct feature learning for these emotions.
  • Overall, the model demonstrates a balanced capability across different emotions with minor confusions, which might be improved with more training data or further tuning of the model architecture and hyperparameters.

Conclusion: The custom complex CNN model, with an accuracy of 81%, outperformed transfer learning architectures, providing an efficient and accurate solution for facial emotion recognition in grayscale images.¶

Insights¶

Refined insights:¶

  • What are the most meaningful insights from the data relevant to the problem?
  1. Models show varying degrees of success, with some like the complex CNN achieving a high level of accuracy (over 80%), suggesting that deeper architectures can capture better the features in facial expressions.

  2. Across models, 'happy' and 'surprise' emotions are generally identified with high accuracy, implying that these emotions have distinct features that are easily captured by the CNN layers.

  3. 'Neutral' and 'sad' emotions are more challenging for the models to distinguish, often being confused with one another.

  4. Transfer learning models did not outperform the custom complex CNN, which suggests that, for this specific dataset and problem, custom-designed architectures can be more effective than off-the-shelf pre-trained models.

Comparison of various techniques and their relative performance:¶

  • How do different techniques perform? Which one is performing relatively better? Is there scope to improve the performance further?
  1. The custom complex CNN model, with additional convolutional layers and parameters, performed the best, achieving an accuracy over 80%. This suggests that custom-tailored architectures can capture the nuances of this particular task more effectively than pre-trained models.

  2. Among the transfer learning models utilized, such as VGG16, ResNet50V2, and EfficientNetV2B0, none outperformed the custom complex CNN model significantly. These models, although powerful, may not have provided substantial improvements due to the specific nature of the dataset, which consists of small grayscale images where fine-tuned features specific to facial emotion recognition might be more beneficial than the generalized features learned from large, diverse datasets these models were trained on.

  3. There may be scope to improve performance:

  • Data Augmentation: Testing more sophisticated data augmentation strategies might help the model generalize better, particularly for underperforming classes.
  • Increased Data: More training data, particularly for the underrepresented and challenging emotions, could improve model learning.
  • Hyperparameter Tuning: Continue tuning the models hyperparameters could optimize model performance.
  • Model creation: Continue creating new custom models to outperform the ones generated.

Proposal for the final solution design:¶

  • What model do you propose to be adopted? Why is this the best solution to adopt?

  • The custom complex CNN model is proposed for adoption as the final solution.

  • The model is specifically designed for the task, with layers and features that detects and recognized the characteristics of facial emotions in grayscale images.

  • It has demonstrated the highest accuracy among all tested models, including transfer learning models.

  • It remains computationally efficient compared to larger transfer learning models, making it more practical for deployment in real-world applications where resources may be limited.

  • As a custom model, it offers greater flexibility for further tuning and modification, which could be beneficial for ongoing optimization and adaptation to new data or use cases.

  • Finally, to be noted that incorporating a facial emotion detection model into real-world applications, such as improving user experience in software interfaces or helping psychological analysis, we need to be really careful about the ethical considerations surrounding its deployment, such as privacy concerns and potential biases.

Recommendations for Implementation¶

Key Recommendations¶

  • Data Augmentation: Implement advanced data augmentation techniques to enhance model robustness, especially for less accurate classifications.
  • Expand Dataset: Collect and integrate a larger and more diverse dataset to improve the model's learning and generalization capabilities.
  • Hyperparameter Optimization: Invest time in fine-tuning the model's hyperparameters to achieve the best possible performance.
  • Model Innovation: Develop new custom models that may provide better results than existing ones, taking into account the specific nuances of the problem.

Actionables for Stakeholders¶

  • Quality Assurance: Ensure the dataset's integrity and diversity to mitigate quality issues that could affect model training.
  • Monitoring and Iteration: Regularly monitor model performance in real-world applications and iterate on the model as needed.
  • Ethical and Privacy Review: Conduct thorough reviews to address any potential ethical and privacy concerns prior to deployment.

Expected Benefit/Cost¶

  • User Experience Improvement: Enhanced user interactions in digital platforms, potentially leading to increased user satisfaction and engagement.
  • Cost of Data Acquisition: Investment may be required to procure or generate a larger and more diverse dataset.
  • Computational Resources: Depending on the model complexity, increased computational power may be necessary, incurring higher costs.

Key Risks and Challenges¶

  • Data Sensitivity: Managing sensitive user data responsibly to uphold privacy standards.
  • Model Generalization: Ensuring the model performs well across diverse real-world scenarios, not just on the training dataset.
  • Bias Mitigation: Continuously working to identify and mitigate biases in model predictions.

Further Analysis and Associated Problems¶

  • Ongoing Evaluation: Continued analysis of model performance against new data and in varying contexts.
  • Complementary Solutions: Investigation into additional solutions that could work alongside the emotion detection model to improve overall system effectiveness

Appendix¶

Project Code Repository Overview¶

The Facial Emotion Detection project leverages a comprehensive code repository hosted on GitHub, serving as a central hub for all the developmental, testing, and final submission artifacts. The repository is structured to facilitate efficient experimentation with model configurations and hyperparameters, which is critical for the iterative process of machine learning model development.

Final Notebook and Batch Testing¶

The centerpiece of the repository is the Final Submission Notebook, which contains the fully executed code along with outputs demonstrating the performance of the final model. This notebook provides transparency and reproducibility, two essential aspects of any data science workflow.

To expedite the model testing phase, the repository includes scripts that enable batch testing of multiple hyperparameter configurations. This approach is delineated in the repository's README file, which outlines how to replicate the model generation process. The corresponding hyperparameters and their permutations are defined within the batch_config.yaml file. This method significantly reduces manual overhead and ensures a methodical exploration of the model's parameter space.

The repository also showcases the complex CNN model configuration in complex_1.yaml, which was identified as the top performer in the project. It is through these YAML configurations that batch processing was enabled, allowing for a comprehensive and automated model testing regime.

Lastly, the results of these model evaluations can be found consolidated under the results directory, providing a quick reference to the performance metrics of each tested model. This structured and systematic approach to model testing underscores the robustness and diligence applied throughout the project lifecycle.